diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b4dc142..af9b215 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -52,7 +52,7 @@ jobs:
 
       # Step 7: Upload Coverage Report as an Artifact (optional)
       - name: Upload Coverage Report
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: coverage-report
           path: htmlcov/
diff --git a/.gitignore b/.gitignore
index 81a8d8c..1015f33 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
+project_data/
 .python-version
 .vscode/
 .DS_Store
diff --git a/poetry.lock b/poetry.lock
index 4de8009..4829c9d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,14 +1,153 @@
 # This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.9"
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+]
+
+[[package]]
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+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.9"
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+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
 [[package]]
 name = "anyio"
-version = "4.7.0"
+version = "4.9.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
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+    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
+    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
 ]
 
 [package.dependencies]
@@ -18,8 +157,8 @@ sniffio = ">=1.1"
 typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -126,64 +265,76 @@ test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "async-lru"
-version = "2.0.4"
+version = "2.0.5"
 description = "Simple LRU cache for asyncio"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
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+    {file = "async_lru-2.0.5-py3-none-any.whl", hash = "sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943"},
+    {file = "async_lru-2.0.5.tar.gz", hash = "sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb"},
 ]
 
 [package.dependencies]
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+
+[[package]]
+name = "async-timeout"
+version = "5.0.1"
+description = "Timeout context manager for asyncio programs"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
 
 [[package]]
 name = "attrs"
-version = "24.3.0"
+version = "25.3.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.8"
 files = [
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-docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"]
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "babel"
-version = "2.16.0"
+version = "2.17.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
+dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"]
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.3"
+version = "4.13.3"
 description = "Screen-scraping library"
 optional = false
-python-versions = ">=3.6.0"
+python-versions = ">=3.7.0"
 files = [
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+    {file = "beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
+typing-extensions = ">=4.0.0"
 
 [package.extras]
 cchardet = ["cchardet"]
@@ -250,20 +401,32 @@ files = [
 ]
 
 [package.dependencies]
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
 webencodings = "*"
 
 [package.extras]
 css = ["tinycss2 (>=1.1.0,<1.5)"]
 
+[[package]]
+name = "cachetools"
+version = "5.5.2"
+description = "Extensible memoizing collections and decorators"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "certifi"
-version = "2024.12.14"
+version = "2025.1.31"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [[package]]
@@ -501,73 +664,74 @@ test = ["pytest"]
 
 [[package]]
 name = "coverage"
-version = "7.6.9"
+version = "7.7.0"
 description = "Code coverage measurement for Python"
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 python-versions = ">=3.9"
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@@ -576,50 +740,65 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
 [package.extras]
 toml = ["tomli"]
 
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 name = "decorator"
-version = "5.1.1"
+version = "5.2.1"
 description = "Decorators for Humans"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
 files = [
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@@ -633,6 +812,48 @@ files = [
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 ]
 
+[[package]]
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+version = "1.2.18"
+description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+files = [
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+]
+
+[package.dependencies]
+wrapt = ">=1.10,<2"
+
+[package.extras]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools", "tox"]
+
+[[package]]
+name = "deprecation"
+version = "2.1.0"
+description = "A library to handle automated deprecations"
+optional = false
+python-versions = "*"
+files = [
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+
+[package.dependencies]
+packaging = "*"
+
+[[package]]
+name = "dirtyjson"
+version = "1.0.8"
+description = "JSON decoder for Python that can extract data from the muck"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "distlib"
 version = "0.3.9"
@@ -644,6 +865,31 @@ files = [
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 ]
 
+[[package]]
+name = "distro"
+version = "1.9.0"
+description = "Distro - an OS platform information API"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[[package]]
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+version = "0.2.2"
+description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+tests = ["pytest"]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.2"
@@ -660,13 +906,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "executing"
-version = "2.1.0"
+version = "2.2.0"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -688,20 +934,31 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.16.1"
+version = "3.18.0"
 description = "A platform independent file lock."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
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-testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"]
 typing = ["typing-extensions (>=4.12.2)"]
 
+[[package]]
+name = "filetype"
+version = "1.2.0"
+description = "Infer file type and MIME type of any file/buffer. No external dependencies."
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "fqdn"
 version = "1.5.1"
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 ]
 
+[[package]]
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+version = "1.5.0"
+description = "A list-like structure which implements collections.abc.MutableSequence"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+docs = ["Sphinx", "furo"]
+test = ["objgraph", "psutil"]
+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -724,6 +1266,32 @@ files = [
     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
 ]
 
+[[package]]
+name = "h2"
+version = "4.2.0"
+description = "Pure-Python HTTP/2 protocol implementation"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "h2-4.2.0-py3-none-any.whl", hash = "sha256:479a53ad425bb29af087f3458a61d30780bc818e4ebcf01f0b536ba916462ed0"},
+    {file = "h2-4.2.0.tar.gz", hash = "sha256:c8a52129695e88b1a0578d8d2cc6842bbd79128ac685463b887ee278126ad01f"},
+]
+
+[package.dependencies]
+hpack = ">=4.1,<5"
+hyperframe = ">=6.1,<7"
+
+[[package]]
+name = "hpack"
+version = "4.1.0"
+description = "Pure-Python HPACK header encoding"
+optional = false
+python-versions = ">=3.9"
+files = [
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+    {file = "hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca"},
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.7"
@@ -759,6 +1327,7 @@ files = [
 [package.dependencies]
 anyio = "*"
 certifi = "*"
+h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""}
 httpcore = "==1.*"
 idna = "*"
 
@@ -769,15 +1338,26 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "hyperframe"
+version = "6.1.0"
+description = "Pure-Python HTTP/2 framing"
+optional = false
+python-versions = ">=3.9"
+files = [
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+    {file = "hyperframe-6.1.0.tar.gz", hash = "sha256:f630908a00854a7adeabd6382b43923a4c4cd4b821fcb527e6ab9e15382a3b08"},
+]
+
 [[package]]
 name = "identify"
-version = "2.6.3"
+version = "2.6.9"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "identify-2.6.9-py2.py3-none-any.whl", hash = "sha256:c98b4322da415a8e5a70ff6e51fbc2d2932c015532d77e9f8537b4ba7813b150"},
+    {file = "identify-2.6.9.tar.gz", hash = "sha256:d40dfe3142a1421d8518e3d3985ef5ac42890683e32306ad614a29490abeb6bf"},
 ]
 
 [package.extras]
@@ -799,13 +1379,13 @@ all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2
 
 [[package]]
 name = "iniconfig"
-version = "2.0.0"
+version = "2.1.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"},
+    {file = "iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7"},
 ]
 
 [[package]]
@@ -843,13 +1423,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.31.0"
+version = "8.34.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -928,13 +1508,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.5"
+version = "3.1.6"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -943,6 +1523,102 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
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+version = "0.9.0"
+description = "Fast iterable JSON parser."
+optional = false
+python-versions = ">=3.8"
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+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
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 version = "0.10.0"
@@ -1055,17 +1731,18 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"
 
 [[package]]
 name = "jupyter-events"
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+version = "0.12.0"
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 [package.dependencies]
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 python-json-logger = ">=2.0.4"
 pyyaml = ">=5.3"
 referencing = "*"
@@ -1149,13 +1826,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.3.4"
+version = "4.3.6"
 description = "JupyterLab computational environment"
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 python-versions = ">=3.8"
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+
 [[package]]
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 version = "0.1.7"
@@ -1301,17 +2265,145 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
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+
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+
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
@@ -1347,18 +2439,18 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.16.4"
+version = "7.16.6"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "!=5.0.0"
+bleach = {version = "!=5.0.0", extras = ["css"]}
 defusedxml = "*"
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
@@ -1370,7 +2462,6 @@ nbformat = ">=5.7"
 packaging = "*"
 pandocfilters = ">=1.4.1"
 pygments = ">=2.4.1"
-tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
@@ -1414,6 +2505,50 @@ files = [
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 ]
 
+[[package]]
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+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.10"
+files = [
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+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
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+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
+[[package]]
+name = "nltk"
+version = "3.9.1"
+description = "Natural Language Toolkit"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
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+regex = ">=2021.8.3"
+tqdm = "*"
+
+[package.extras]
+all = ["matplotlib", "numpy", "pyparsing", "python-crfsuite", "requests", "scikit-learn", "scipy", "twython"]
+corenlp = ["requests"]
+machine-learning = ["numpy", "python-crfsuite", "scikit-learn", "scipy"]
+plot = ["matplotlib"]
+tgrep = ["pyparsing"]
+twitter = ["twython"]
+
 [[package]]
 name = "nodeenv"
 version = "1.9.1"
@@ -1487,6 +2622,32 @@ files = [
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 ]
 
+[[package]]
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+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
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+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+jiter = ">=0.4.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+tqdm = ">4"
+typing-extensions = ">=4.11,<5"
+
+[package.extras]
+datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
+realtime = ["websockets (>=13,<15)"]
+voice-helpers = ["numpy (>=2.0.2)", "sounddevice (>=0.5.1)"]
+
 [[package]]
 name = "overrides"
 version = "7.7.0"
@@ -1646,21 +2807,109 @@ files = [
 [package.dependencies]
 ptyprocess = ">=0.5"
 
+[[package]]
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+description = "Python Imaging Library (Fork)"
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-type = ["mypy (>=1.11.2)"]
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+type = ["mypy (>=1.14.1)"]
 
 [[package]]
 name = "pluggy"
@@ -1677,6 +2926,23 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
+[[package]]
+name = "postgrest"
+version = "0.19.3"
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+optional = false
+python-versions = "<4.0,>=3.9"
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+pydantic = ">=1.9,<3.0"
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+
 [[package]]
 name = "pre-commit"
 version = "4.0.1"
@@ -1711,46 +2977,146 @@ twisted = ["twisted"]
 
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 [package.dependencies]
 wcwidth = "*"
 
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 [[package]]
 name = "requests"
@@ -2229,116 +3833,130 @@ files = [
 
 [[package]]
 name = "rpds-py"
-version = "0.22.3"
+version = "0.23.1"
 description = "Python bindings to Rust's persistent data structures (rpds)"
 optional = false
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+
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 version = "1.8.3"
@@ -2357,23 +3975,23 @@ win32 = ["pywin32"]
 
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 [[package]]
 name = "six"
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+
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+mssql-pymssql = ["pymssql"]
+mssql-pyodbc = ["pyodbc"]
+mypy = ["mypy (>=0.910)"]
+mysql = ["mysqlclient (>=1.4.0)"]
+mysql-connector = ["mysql-connector-python"]
+oracle = ["cx_oracle (>=8)"]
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+pymysql = ["pymysql"]
+sqlcipher = ["sqlcipher3_binary"]
+
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 version = "0.6.3"
@@ -2427,6 +4140,97 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
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+
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+description = "An Enum that inherits from str."
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+python-versions = "*"
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+release = ["twine"]
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+
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+python-versions = "<4.0,>=3.9"
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+
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+
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+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.8"
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+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
 [[package]]
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 version = "0.18.1"
@@ -2448,6 +4252,53 @@ docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
 typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
+[[package]]
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+optional = false
+python-versions = ">=3.9"
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+requests = ">=2.26.0"
+
+[package.extras]
+blobfile = ["blobfile (>=2)"]
+
 [[package]]
 name = "tinycss2"
 version = "1.4.0"
@@ -2527,6 +4378,27 @@ files = [
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 ]
 
+[[package]]
+name = "tqdm"
+version = "4.67.1"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
+discord = ["requests"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "traitlets"
 version = "5.14.3"
@@ -2564,15 +4436,44 @@ files = [
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+[[package]]
+name = "typing-inspect"
+version = "0.9.0"
+description = "Runtime inspection utilities for typing module."
+optional = false
+python-versions = "*"
+files = [
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+    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
+]
+
+[package.dependencies]
+mypy-extensions = ">=0.3.0"
+typing-extensions = ">=3.7.4"
+
+[[package]]
+name = "typing-inspection"
+version = "0.4.0"
+description = "Runtime typing introspection tools"
+optional = false
+python-versions = ">=3.9"
+files = [
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+]
+
+[package.dependencies]
+typing-extensions = ">=4.12.0"
+
 [[package]]
 name = "tzdata"
-version = "2024.2"
+version = "2025.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
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+    {file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"},
+    {file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"},
 ]
 
 [[package]]
@@ -2608,13 +4509,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.28.0"
+version = "20.29.3"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2675,7 +4576,269 @@ docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
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+    {file = "yarl-1.18.3-py3-none-any.whl", hash = "sha256:b57f4f58099328dfb26c6a771d09fb20dbbae81d20cfb66141251ea063bd101b"},
+    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<4.0"
-content-hash = "d3c1d17a29e1f4ddcfc58f3e0ffe750d5ae6741c7cd37f800b0f0e3e4dba281d"
+content-hash = "068fca6482d40a13df1589bbf01fcda737f7e7c8562204d139fd845a757a2ae2"
diff --git a/pyproject.toml b/pyproject.toml
index 47a037b..9f557d2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,21 +1,34 @@
+[tool.poetry.scripts]
+sequestrae = "sequestrae_engine.cli.main:main"
+
 [tool.poetry]
 name = "sequestrae-engine"
 version = "0.1.0"
-description = "A Biochar Carbon Removal Assessment Engine for evaluating carbon sequestration potential."
+description = "A Biochar Carbon Removal Assessment Engine for evaluating project risk."
 authors = ["Du Phan <du-phan@users.noreply.github.com>"]
 license = "MIT"
 readme = "README.md"
 homepage = "https://github.com/du-phan/sequestrae-engine"
 repository = "https://github.com/du-phan/sequestrae-engine"
 keywords = ["biochar", "carbon removal", "assessment", "engine", "sequestration"]
+packages = [
+    { include = "sequestrae_engine" }
+]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 numpy = ">=1.24,<2.0"
 pandas = "^2.0"
-pydantic = "^1.10.2"
+pydantic = "^2.5.2"
 jsonschema = "^4.16.0"
 jupyterlab = "^4.0.5"
+llama-index = "^0.12.16"
+llama-parse = "^0.6"
+python-dotenv = "^1.0.0"
+openai = "^1.0.0"
+mistralai = "^1.3"
+google-genai = "^1.0.0"
+supabase = "^2.10.0"
 
 [tool.poetry.dev-dependencies]
 black = "24.10.0"
diff --git a/sequestrae_engine/cli/__init__.py b/sequestrae_engine/cli/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/sequestrae_engine/cli/commands.py b/sequestrae_engine/cli/commands.py
new file mode 100644
index 0000000..72888ce
--- /dev/null
+++ b/sequestrae_engine/cli/commands.py
@@ -0,0 +1,652 @@
+import logging
+import os
+import time
+from functools import wraps
+from pathlib import Path
+from time import sleep
+
+from sequestrae_engine.db.client import SupabaseClient
+from sequestrae_engine.db.scripts.populate_audit_reports import populate_feedstock_evaluation_table
+from sequestrae_engine.document_parsing.extractors import AuditReportExtractor
+from sequestrae_engine.document_parsing.parser import PDFToMarkdownParser
+
+# Configure logger
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+def retry_on_error(max_retries=3, delay=5):
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            retries = 0
+            while retries < max_retries:
+                try:
+                    return func(*args, **kwargs)
+                except Exception as e:
+                    retries += 1
+                    if retries == max_retries:
+                        raise e
+                    logger.warning(
+                        f"Error occurred: {str(e)}. Retrying in {delay} seconds... (Attempt {retries}/{max_retries})"
+                    )
+                    sleep(delay)
+            return None
+
+        return wrapper
+
+    return decorator
+
+
+def parse_pdfs_command(api_key, projects_root_dir, limit=5):
+    if limit is None:
+        limit = 5
+    logger.info(f"Max number of files to process: {limit}")
+
+    if not api_key:
+        logger.error("LLAMA_API_KEY is required")
+        return 1
+
+    parser = PDFToMarkdownParser(api_key=api_key)
+    projects_path = Path(projects_root_dir)
+
+    if not projects_path.exists():
+        logger.error(f"Directory not found at {projects_path}")
+        return 1
+
+    pdf_count = 0
+    for folder_path in projects_path.iterdir():
+        if not folder_path.name.startswith(".") and folder_path.is_dir() and pdf_count < limit:
+            for pdf_path in folder_path.glob("*.pdf"):
+                if pdf_path.is_file() and "report" in pdf_path.stem.lower():
+                    pdf_count += 1
+                    parser.parse_pdf(pdf_path)
+
+    logger.info(f"Successfully processed {pdf_count} PDF files")
+    return 0
+
+
+def extract_audit_information_command(api_key, projects_root_dir, limit=100):
+    if limit is None:
+        limit = 100
+    logger.info(f"Max number of files to process: {limit}")
+
+    if not api_key:
+        logger.error("MISTRAL_API_KEY is required")
+        return 1
+
+    extractor = AuditReportExtractor(mistral_api_key=api_key)
+    projects_path = Path(projects_root_dir)
+
+    if not projects_path.exists():
+        logger.error(f"Directory not found at {projects_path}")
+        return 1
+
+    markdown_count = 0
+    for folder_path in projects_path.iterdir():
+        if not folder_path.name.startswith(".") and folder_path.is_dir() and markdown_count < limit:
+            for md_path in folder_path.glob("*.md"):
+                if md_path.is_file() and "report" in md_path.stem.lower():
+                    markdown_count += 1
+                    try:
+                        retry_on_error()(extractor.parse_audit_report)(audit_report_path=md_path)
+                    except Exception as e:
+                        logger.error(f"Error processing {md_path}: {str(e)}")
+                    time.sleep(1)  # Sleep for 1 second to avoid rate limiting
+
+    logger.info(f"Successfully processed {markdown_count} markdown files")
+    return 0
+
+
+def evaluate_feedstock_sustainability_command(api_key, projects_root_dir, limit=100):
+    if limit is None:
+        limit = 100
+    logger.info(f"Max number of files to process: {limit}")
+
+    if not api_key:
+        logger.error("MISTRAL_API_KEY is required")
+        return 1
+
+    extractor = AuditReportExtractor(mistral_api_key=api_key)
+    projects_path = Path(projects_root_dir)
+
+    if not projects_path.exists():
+        logger.error(f"Directory not found at {projects_path}")
+        return 1
+
+    markdown_count = 0
+    for folder_path in projects_path.iterdir():
+        if not folder_path.name.startswith(".") and folder_path.is_dir() and markdown_count < limit:
+            for md_path in folder_path.glob("*.md"):
+                if md_path.is_file() and "report" in md_path.stem.lower():
+                    markdown_count += 1
+                    try:
+                        retry_on_error()(extractor.analyze_feedstock_sustainability)(
+                            audit_path=md_path
+                        )
+                    except Exception as e:
+                        logger.error(f"Error processing {md_path}: {str(e)}")
+                    time.sleep(1)  # Sleep for 1 second to avoid rate limiting
+
+    logger.info(f"Successfully processed {markdown_count} markdown files")
+    return 0
+
+
+def populate_feedstock_evaluation_command(supabase_url, supabase_api_key, projects_root_dir):
+    if not supabase_url or not supabase_api_key:
+        logger.error("Supabase URL and api key are required")
+        return 1
+
+    try:
+        supabase_client = SupabaseClient.get_client(supabase_url, supabase_api_key)
+        populate_feedstock_evaluation_table(projects_root_dir, supabase_client)
+        logger.info("Successfully populated feedstock evaluation table")
+        return 0
+    except Exception as e:
+        logger.error(f"Error populating feedstock evaluation table: {str(e)}")
+        return 1
+
+
+def analyze_due_diligence_command(gemini_api_key, mistral_api_key, projects_root_dir):
+    """
+    Process all PDFs in project subfolders and analyze due diligence criteria.
+
+    Args:
+        gemini_api_key: API key for PDF parsing
+        mistral_api_key: API key for analysis
+        projects_root_dir: Root directory containing project folders
+    """
+    # max_num_folder = 60
+
+    if not gemini_api_key or not mistral_api_key:
+        logger.error("Both GEMINI_API_KEY and MISTRAL_API_KEY are required")
+        return 1
+
+    projects_path = Path(projects_root_dir)
+    if not projects_path.exists():
+        logger.error(f"Directory not found at {projects_path}")
+        return 1
+
+    pdf_parser = PDFToMarkdownParser(gemini_api_key=gemini_api_key)
+    audit_extractor = AuditReportExtractor(mistral_api_key=mistral_api_key)
+
+    # Count total subfolders (excluding hidden folders)
+    total_folders = sum(
+        1
+        for folder in projects_path.iterdir()
+        if folder.is_dir() and not folder.name.startswith(".")
+    )
+
+    logger.info(f"Found {total_folders} project folders to process")
+
+    processed_folder = 1
+    for folder_path in projects_path.iterdir():
+        # if processed_folder >= max_num_folder:
+        # break
+
+        if not folder_path.name.startswith(".") and folder_path.is_dir():
+            project_name = "_".join(folder_path.name.split())
+            logger.info(f"Analyzing project {processed_folder}/{total_folders}: {project_name} ...")
+
+            # Parse PDFs in folder
+            start_time = time.time()
+            pdf_parser.parse_pdf_folder(folder_path, overwrite=False)
+            logger.info(
+                f"--------- PDF processing completed in {round((time.time() - start_time)/60, 2)} minutes ---------"
+            )
+
+            # Analyze due diligence
+            markdown_document_path = os.path.join(
+                folder_path, "parsed_markdown", f"concatenated_documentation_{pdf_parser.model}.md"
+            )
+            start_time = time.time()
+            retry_on_error()(audit_extractor.analyze_due_diligence_criteria)(
+                project_name=project_name,
+                markdown_document_path=markdown_document_path,
+                overwrite=False,
+            )
+            logger.info(
+                f"--------- Due diligence analysis complete in {round((time.time() - start_time)/60, 2)} minutes."
+            )
+            processed_folder += 1
+
+        time.sleep(1)
+
+    logger.info("Completed processing all projects")
+    return 0
+
+
+def create_subtopic_summaries_command(mistral_api_key, projects_root_dir):
+    """
+    Process all analysis JSON files in project subfolders and create subtopic summaries.
+
+    Args:
+        mistral_api_key: API key for analysis
+        projects_root_dir: Root directory containing project folders
+    """
+    if not mistral_api_key:
+        logger.error("MISTRAL_API_KEY is required")
+        return 1
+
+    projects_path = Path(projects_root_dir)
+    if not projects_path.exists():
+        logger.error(f"Directory not found at {projects_path}")
+        return 1
+
+    audit_extractor = AuditReportExtractor(mistral_api_key=mistral_api_key)
+
+    # Count total subfolders (excluding hidden folders)
+    total_folders = sum(
+        1
+        for folder in projects_path.iterdir()
+        if folder.is_dir() and not folder.name.startswith(".")
+    )
+
+    logger.info(f"Found {total_folders} project folders to process")
+
+    processed_folder = 1
+    for folder_path in projects_path.iterdir():
+        if not folder_path.name.startswith(".") and folder_path.is_dir():
+            project_name = "_".join(folder_path.name.split())
+            logger.info(
+                f"Processing project {processed_folder}/{total_folders}: {project_name} ..."
+            )
+
+            # Find analysis file
+            analysis_path = (
+                folder_path / "analysis" / f"{project_name}_analysis_mistral-large-latest.json"
+            )
+            if not analysis_path.exists():
+                logger.warning(f"Analysis file not found for {project_name}, skipping...")
+                continue
+
+            # Create summaries
+            start_time = time.time()
+            try:
+                retry_on_error()(audit_extractor.create_subtopic_summaries)(
+                    analysis_json_path=str(analysis_path),
+                    overwrite=False,
+                )
+                logger.info(
+                    f"--------- Subtopic summaries created in {round((time.time() - start_time)/60, 2)} minutes."
+                )
+            except Exception as e:
+                logger.error(f"Error processing {project_name}: {str(e)}")
+
+            processed_folder += 1
+            time.sleep(1)
+
+    logger.info("Completed processing all projects")
+    return 0
+
+
+def create_topic_summaries_command(mistral_api_key, projects_root_dir):
+    """
+    Process all subtopic summary JSON files in project subfolders and create topic summaries.
+
+    Args:
+        mistral_api_key: API key for analysis
+        projects_root_dir: Root directory containing project folders
+    """
+    if not mistral_api_key:
+        logger.error("MISTRAL_API_KEY is required")
+        return 1
+
+    projects_path = Path(projects_root_dir)
+    if not projects_path.exists():
+        logger.error(f"Directory not found at {projects_path}")
+        return 1
+
+    audit_extractor = AuditReportExtractor(mistral_api_key=mistral_api_key)
+
+    # Count total subfolders (excluding hidden folders)
+    total_folders = sum(
+        1
+        for folder in projects_path.iterdir()
+        if folder.is_dir() and not folder.name.startswith(".")
+    )
+
+    logger.info(f"Found {total_folders} project folders to process")
+
+    processed_folder = 1
+    for folder_path in projects_path.iterdir():
+        if not folder_path.name.startswith(".") and folder_path.is_dir():
+            project_name = "_".join(folder_path.name.split())
+            logger.info(
+                f"Processing project {processed_folder}/{total_folders}: {project_name} ..."
+            )
+
+            # Find subtopic summary file
+            subtopic_summary_path = (
+                folder_path
+                / "analysis"
+                / f"{project_name}_analysis_mistral-large-latest_subtopic_summaries.json"
+            )
+            if not subtopic_summary_path.exists():
+                logger.warning(f"Subtopic summary file not found for {project_name}, skipping...")
+                continue
+
+            # Create topic summaries
+            start_time = time.time()
+            try:
+                retry_on_error()(audit_extractor.create_topic_summaries)(
+                    subtopic_summaries_json_path=str(subtopic_summary_path), overwrite=False
+                )
+                logger.info(
+                    f"--------- Topic summaries created in {round((time.time() - start_time)/60, 2)} minutes."
+                )
+            except Exception as e:
+                logger.error(f"Error processing {project_name}: {str(e)}")
+
+            processed_folder += 1
+            time.sleep(1)
+
+    logger.info("Completed processing all projects")
+    return 0
+
+
+def populate_registry_analysis_command(supabase_url, supabase_api_key, registries_dir):
+    """
+    Process multiple registry folders and populate Supabase with analysis data.
+
+    Args:
+        supabase_url: Supabase project URL
+        supabase_api_key: Supabase API key
+        registries_dir: Root directory containing registry folders
+    """
+    if not supabase_url or not supabase_api_key:
+        logger.error("Supabase URL and API key are required")
+        return 1
+
+    try:
+        from sequestrae_engine.db.scripts.populate_registry_analysis import (
+            populate_registry_analysis,
+        )
+
+        # Initialize Supabase client
+        supabase_client = SupabaseClient.get_client(supabase_url, supabase_api_key)
+
+        # Process all registries at once
+        result = populate_registry_analysis(registries_dir=registries_dir, client=supabase_client)
+
+        # Log statistics
+        for table_name, content_list in result.items():
+            logger.info(f"Total records inserted into {table_name} table: {len(content_list)}")
+
+        logger.info("Successfully completed processing all registries")
+        return 0
+
+    except Exception as e:
+        logger.error(f"Error populating registry analysis data: {str(e)}")
+        return 1
+
+
+def extract_project_overview_command(mistral_api_key, projects_root_dir):
+    """
+    Process all markdown documents in project subfolders and extract project overview data.
+
+    Args:
+        mistral_api_key: API key for analysis
+        projects_root_dir: Root directory containing project folders
+    """
+    if not mistral_api_key:
+        logger.error("MISTRAL_API_KEY is required")
+        return 1
+
+    projects_path = Path(projects_root_dir)
+    if not projects_path.exists():
+        logger.error(f"Directory not found at {projects_path}")
+        return 1
+
+    audit_extractor = AuditReportExtractor(mistral_api_key=mistral_api_key)
+
+    # Count total subfolders (excluding hidden folders)
+    total_folders = sum(
+        1
+        for folder in projects_path.iterdir()
+        if folder.is_dir() and not folder.name.startswith(".")
+    )
+
+    logger.info(f"Found {total_folders} project folders to process")
+
+    processed_folder = 1
+    for folder_path in projects_path.iterdir():
+        if not folder_path.name.startswith(".") and folder_path.is_dir():
+            project_name = "_".join(folder_path.name.split())
+            logger.info(
+                f"Processing project {processed_folder}/{total_folders}: {project_name} ..."
+            )
+
+            # Find markdown document
+            markdown_document_path = os.path.join(
+                folder_path,
+                "parsed_markdown",
+                f"concatenated_documentation_gemini-2.0-flash-001.md",
+            )
+
+            if not os.path.exists(markdown_document_path):
+                logger.warning(
+                    f"Markdown document not found at {markdown_document_path}, skipping..."
+                )
+                continue
+
+            # Extract project overview
+            start_time = time.time()
+            try:
+                retry_on_error()(audit_extractor.extract_project_overview_data)(
+                    project_name=project_name,
+                    markdown_document_path=markdown_document_path,
+                    overwrite=False,
+                )
+                logger.info(
+                    f"--------- Project overview extraction complete in {round((time.time() - start_time)/60, 2)} minutes."
+                )
+            except Exception as e:
+                logger.error(f"Error processing {project_name}: {str(e)}")
+
+            processed_folder += 1
+            time.sleep(1)  # Rate limiting
+
+    logger.info("Completed processing all projects")
+    return 0
+
+
+def create_project_main_insights_command(mistral_api_key, projects_root_dir):
+    """
+    Process all topic summary JSON files in project subfolders and create project main insights.
+
+    Args:
+        mistral_api_key: API key for analysis
+        projects_root_dir: Root directory containing project folders
+    """
+    if not mistral_api_key:
+        logger.error("MISTRAL_API_KEY is required")
+        return 1
+
+    projects_path = Path(projects_root_dir)
+    if not projects_path.exists():
+        logger.error(f"Directory not found at {projects_path}")
+        return 1
+
+    audit_extractor = AuditReportExtractor(mistral_api_key=mistral_api_key)
+
+    # Count total subfolders (excluding hidden folders)
+    total_folders = sum(
+        1
+        for folder in projects_path.iterdir()
+        if folder.is_dir() and not folder.name.startswith(".")
+    )
+
+    logger.info(f"Found {total_folders} project folders to process")
+
+    processed_folder = 1
+    for folder_path in projects_path.iterdir():
+        if not folder_path.name.startswith(".") and folder_path.is_dir():
+            project_name = "_".join(folder_path.name.split())
+            logger.info(
+                f"Processing project {processed_folder}/{total_folders}: {project_name} ..."
+            )
+
+            # Find topic summary file
+            topic_summary_path = (
+                folder_path
+                / "analysis"
+                / f"{project_name}_analysis_mistral-large-latest_subtopic_summaries_topic_summaries.json"
+            )
+            if not topic_summary_path.exists():
+                logger.warning(f"Topic summary file not found for {project_name}, skipping...")
+                continue
+
+            # Create project main insights
+            start_time = time.time()
+            try:
+                retry_on_error()(audit_extractor.create_topic_main_insights)(
+                    topic_summaries_json_path=str(topic_summary_path),
+                    overwrite=True,
+                )
+                logger.info(
+                    f"--------- Project main insights created in {round((time.time() - start_time)/60, 2)} minutes."
+                )
+            except Exception as e:
+                logger.error(f"Error processing {project_name}: {str(e)}")
+
+            processed_folder += 1
+            time.sleep(1)  # Rate limiting
+
+    logger.info("Completed processing all projects")
+    return 0
+
+
+def process_project_command(gemini_api_key, mistral_api_key, project_dir, overwrite=False):
+    """
+    Run the complete analysis pipeline for a single project folder.
+
+    This function:
+    1. Parses PDFs in the project folder to markdown
+    2. Runs the full analysis pipeline on the parsed markdown
+
+    Args:
+        gemini_api_key: API key for PDF parsing
+        mistral_api_key: API key for analysis
+        project_dir: Path to the specific project folder to process
+        overwrite: Whether to overwrite existing output files (default: False)
+
+    Returns:
+        int: 0 if successful, 1 if there was an error
+    """
+    if not gemini_api_key or not mistral_api_key:
+        logger.error("Both GEMINI_API_KEY and MISTRAL_API_KEY are required")
+        return 1
+
+    project_path = Path(project_dir)
+    if not project_path.exists() or not project_path.is_dir():
+        logger.error(f"Project folder not found at {project_path}")
+        return 1
+
+    project_name = project_path.name
+    logger.info(f"Starting complete analysis pipeline for project: {project_name}")
+    logger.info(f"Overwrite existing files: {'Yes' if overwrite else 'No'}")
+
+    try:
+        # Step 1: Parse PDFs to markdown
+        total_start_time = time.time()
+        start_time = time.time()
+        logger.info("----Parsing PDFs to markdown----")
+        pdf_parser = PDFToMarkdownParser(
+            gemini_api_key=gemini_api_key, project_folder=str(project_path)
+        )
+        retry_on_error()(pdf_parser.parse_pdf_folder)(overwrite=overwrite)
+        logger.info(
+            f"✓ PDF processing completed in {round((time.time() - start_time)/60, 2)} minutes"
+        )
+
+        # Step 2: Run the full analysis pipeline
+        logger.info("----Running analysis pipeline----")
+        report_extractor = AuditReportExtractor(
+            mistral_api_key=mistral_api_key, project_folder=str(project_path), overwrite=overwrite
+        )
+        retry_on_error()(report_extractor.process_project)()
+
+        total_time = time.time() - total_start_time
+        minutes, seconds = divmod(total_time, 60)
+        logger.info(
+            f"Successfully completed processing project {project_name} in {int(minutes)} minutes {seconds:.2f} seconds"
+        )
+        return 0
+
+    except Exception as e:
+        logger.error(f"Error processing project {project_name}: {str(e)}")
+        return 1
+
+
+def process_all_projects_command(
+    gemini_api_key, mistral_api_key, projects_root_dir, overwrite=False
+):
+    """
+    Process all project folders in the given root directory.
+
+    This function:
+    1. Finds all project folders in the specified root directory
+    2. Calls process_project_command for each project folder
+    3. Reports overall statistics
+
+    Args:
+        gemini_api_key: API key for PDF parsing
+        mistral_api_key: API key for analysis
+        projects_root_dir: Path to the directory containing multiple project folders
+        overwrite: Whether to overwrite existing output files (default: False)
+
+    Returns:
+        int: 0 if all projects processed successfully, 1 if there were any errors
+    """
+    if not gemini_api_key or not mistral_api_key:
+        logger.error("Both GEMINI_API_KEY and MISTRAL_API_KEY are required")
+        return 1
+
+    root_path = Path(projects_root_dir)
+    if not root_path.exists() or not root_path.is_dir():
+        logger.error(f"Root directory not found at {root_path}")
+        return 1
+
+    # Find all project folders (excluding hidden folders)
+    project_folders = [
+        folder
+        for folder in root_path.iterdir()
+        if folder.is_dir() and not folder.name.startswith(".")
+    ]
+
+    total_projects = len(project_folders)
+    logger.info(f"Found {total_projects} projects to process")
+
+    if total_projects == 0:
+        logger.warning("No project folders found in the root directory")
+        return 0
+
+    success_count = 0
+    failed_count = 0
+
+    for i, project_folder in enumerate(project_folders, 1):
+        project_name = project_folder.name
+        logger.info(f"Processing project {i}/{total_projects}: {project_name}")
+
+        try:
+            result = process_project_command(
+                gemini_api_key, mistral_api_key, str(project_folder), overwrite
+            )
+            if result == 0:
+                success_count += 1
+            else:
+                failed_count += 1
+                logger.error(f"Failed to process project {project_name}")
+        except Exception as e:
+            failed_count += 1
+            logger.error(f"Exception while processing project {project_name}: {str(e)}")
+
+    logger.info(
+        f"Completed processing all projects. Success: {success_count}, Failed: {failed_count}"
+    )
+
+    # Return success if all projects were processed successfully
+    return 0 if failed_count == 0 else 1
diff --git a/sequestrae_engine/cli/main.py b/sequestrae_engine/cli/main.py
new file mode 100644
index 0000000..29dbc15
--- /dev/null
+++ b/sequestrae_engine/cli/main.py
@@ -0,0 +1,235 @@
+import argparse
+import os
+import sys
+
+from . import commands
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Sequestrae Engine CLI")
+    subparsers = parser.add_subparsers(dest="command")
+
+    # Parse PDFs command
+    parse_pdfs_parser = subparsers.add_parser(
+        "parse-pdf", help="Parse PDF audit report files to Markdown"
+    )
+    parse_pdfs_parser.add_argument("--llama-api-key", help="Llama API key", required=True)
+    parse_pdfs_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    parse_pdfs_parser.add_argument("--limit", help="Maximum number of PDFs to process", type=int)
+    parse_pdfs_parser.set_defaults(
+        func=lambda args: commands.parse_pdfs_command(
+            args.llama_api_key, args.projects_root_dir, args.limit
+        )
+    )
+
+    # Extract audit information command
+    extract_audit_parser = subparsers.add_parser(
+        "extract-audit", help="Extract audit information from markdown report files"
+    )
+    extract_audit_parser.add_argument("--mistral-api-key", help="Mistral API key", required=True)
+    extract_audit_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    extract_audit_parser.add_argument(
+        "--limit", help="Maximum number of files to process", type=int
+    )
+    extract_audit_parser.set_defaults(
+        func=lambda args: commands.extract_audit_information_command(
+            args.mistral_api_key, args.projects_root_dir, args.limit
+        )
+    )
+
+    # Evaluate feedstock sustainability command
+    evaluate_feedstock_parser = subparsers.add_parser(
+        "evaluate-feedstock", help="Evaluate feedstock sustainability from markdown report files"
+    )
+    evaluate_feedstock_parser.add_argument(
+        "--mistral-api-key", help="Mistral API key", required=True
+    )
+    evaluate_feedstock_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    evaluate_feedstock_parser.add_argument(
+        "--limit", help="Maximum number of files to process", type=int
+    )
+    evaluate_feedstock_parser.set_defaults(
+        func=lambda args: commands.evaluate_feedstock_sustainability_command(
+            args.mistral_api_key, args.projects_root_dir, args.limit
+        )
+    )
+
+    # Populate feedstock evaluation table command
+    populate_feedstock_parser = subparsers.add_parser(
+        "populate-feedstock-table", help="Populate Supabase with feedstock evaluation data"
+    )
+    populate_feedstock_parser.add_argument("--supabase-url", help="Supabase URL", required=True)
+    populate_feedstock_parser.add_argument("--supabase-api-key", help="Supabase key", required=True)
+    populate_feedstock_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    populate_feedstock_parser.set_defaults(
+        func=lambda args: commands.populate_feedstock_evaluation_command(
+            args.supabase_url, args.supabase_api_key, args.projects_root_dir
+        )
+    )
+
+    # Due diligence analyze command
+    process_analyze_parser = subparsers.add_parser(
+        "due-diligence-analyze",
+        help="Process PDFs and analyze due diligence criteria for all projects",
+    )
+    process_analyze_parser.add_argument(
+        "--gemini-api-key", help="Gemini API key for PDF parsing", required=True
+    )
+    process_analyze_parser.add_argument(
+        "--mistral-api-key", help="Mistral API key for analysis", required=True
+    )
+    process_analyze_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    process_analyze_parser.set_defaults(
+        func=lambda args: commands.analyze_due_diligence_command(
+            args.gemini_api_key, args.mistral_api_key, args.projects_root_dir
+        )
+    )
+
+    # Create subtopic summaries command
+    create_summaries_parser = subparsers.add_parser(
+        "create-subtopic-summaries", help="Create subtopic summaries from analysis JSON files"
+    )
+    create_summaries_parser.add_argument(
+        "--mistral-api-key", help="Mistral API key for analysis", required=True
+    )
+    create_summaries_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    create_summaries_parser.set_defaults(
+        func=lambda args: commands.create_subtopic_summaries_command(
+            args.mistral_api_key, args.projects_root_dir
+        )
+    )
+
+    # Create topic summaries command
+    create_topic_parser = subparsers.add_parser(
+        "create-topic-summaries", help="Create topic summaries from subtopic summary JSON files"
+    )
+    create_topic_parser.add_argument(
+        "--mistral-api-key", help="Mistral API key for analysis", required=True
+    )
+    create_topic_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    create_topic_parser.set_defaults(
+        func=lambda args: commands.create_topic_summaries_command(
+            args.mistral_api_key, args.projects_root_dir
+        )
+    )
+
+    # Extract project overview command
+    extract_overview_parser = subparsers.add_parser(
+        "extract-project-overview", help="Extract project overview data from markdown documents"
+    )
+    extract_overview_parser.add_argument(
+        "--mistral-api-key", help="Mistral API key for analysis", required=True
+    )
+    extract_overview_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    extract_overview_parser.set_defaults(
+        func=lambda args: commands.extract_project_overview_command(
+            args.mistral_api_key, args.projects_root_dir
+        )
+    )
+
+    # Add new command for populating registry analysis data
+    populate_registry_parser = subparsers.add_parser(
+        "populate-registry-analysis",
+        help="Populate Supabase with analysis data from multiple registry folders",
+    )
+    populate_registry_parser.add_argument("--supabase-url", help="Supabase URL", required=True)
+    populate_registry_parser.add_argument("--supabase-api-key", help="Supabase key", required=True)
+    populate_registry_parser.add_argument(
+        "--registries-dir", help="Root directory containing registry folders", required=True
+    )
+    populate_registry_parser.set_defaults(
+        func=lambda args: commands.populate_registry_analysis_command(
+            args.supabase_url, args.supabase_api_key, args.registries_dir
+        )
+    )
+
+    # Add new command for generating project main insights
+    create_main_insights_parser = subparsers.add_parser(
+        "create-project-insights", help="Create project main insights from topic summary JSON files"
+    )
+    create_main_insights_parser.add_argument(
+        "--mistral-api-key", help="Mistral API key for analysis", required=True
+    )
+    create_main_insights_parser.add_argument(
+        "--projects-root-dir", help="Root directory containing project folders", required=True
+    )
+    create_main_insights_parser.set_defaults(
+        func=lambda args: commands.create_project_main_insights_command(
+            args.mistral_api_key, args.projects_root_dir
+        )
+    )
+
+    # Add new command for processing a single project through the entire pipeline
+    process_project_parser = subparsers.add_parser(
+        "process-project", help="Run the complete analysis pipeline for a single project folder"
+    )
+    process_project_parser.add_argument(
+        "--gemini-api-key", help="Gemini API key for PDF parsing", required=True
+    )
+    process_project_parser.add_argument(
+        "--mistral-api-key", help="Mistral API key for analysis", required=True
+    )
+    process_project_parser.add_argument(
+        "--project-dir", help="Path to the specific project folder to process", required=True
+    )
+    process_project_parser.add_argument(
+        "--overwrite", help="Overwrite existing output files", action="store_true"
+    )
+    process_project_parser.set_defaults(
+        func=lambda args: commands.process_project_command(
+            args.gemini_api_key, args.mistral_api_key, args.project_dir, args.overwrite
+        )
+    )
+
+    # Add new command for processing multiple projects through the entire pipeline
+    process_all_projects_parser = subparsers.add_parser(
+        "process-all-projects",
+        help="Run the complete analysis pipeline for all project folders in a root directory",
+    )
+    process_all_projects_parser.add_argument(
+        "--gemini-api-key", help="Gemini API key for PDF parsing", required=True
+    )
+    process_all_projects_parser.add_argument(
+        "--mistral-api-key", help="Mistral API key for analysis", required=True
+    )
+    process_all_projects_parser.add_argument(
+        "--projects-root-dir",
+        help="Path to the directory containing multiple project folders",
+        required=True,
+    )
+    process_all_projects_parser.add_argument(
+        "--overwrite", help="Overwrite existing output files", action="store_true"
+    )
+    process_all_projects_parser.set_defaults(
+        func=lambda args: commands.process_all_projects_command(
+            args.gemini_api_key, args.mistral_api_key, args.projects_root_dir, args.overwrite
+        )
+    )
+
+    args = parser.parse_args()
+    if hasattr(args, "func"):
+        exit_status = args.func(args)
+    else:
+        parser.print_help()
+        exit_status = 1
+    return exit_status
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sequestrae_engine/core/utilities.py b/sequestrae_engine/core/utilities.py
index 0ff1b2e..e8806ed 100644
--- a/sequestrae_engine/core/utilities.py
+++ b/sequestrae_engine/core/utilities.py
@@ -1,6 +1,7 @@
 import json
 import logging
 import os
+from pathlib import Path
 
 from jsonschema import validate, validators
 
@@ -14,6 +15,21 @@
 logger = logging.getLogger(__name__)
 
 
+def get_json_files(project_folder_path):
+    # Get the project_data directory path
+    base_dir = Path(project_folder_path)
+
+    # Check if directory exists
+    if not base_dir.exists():
+        print(f"Error: Directory not found at {base_dir}")
+        return []
+
+    # Find all JSON files and store paths in a list
+    json_files = [str(file) for file in base_dir.rglob("*.json")]
+
+    return json_files
+
+
 def read_json(file_path):
     """
     Reads a JSON file and returns the data as a dictionary.
@@ -36,6 +52,23 @@ def read_json(file_path):
             raise e
 
 
+def load_json_file(filepath: str) -> dict:
+    try:
+        file_path = Path(filepath)
+        with file_path.open("r", encoding="utf-8") as f:
+            data = json.load(f)
+        return data
+    except FileNotFoundError:
+        logger.error(f"File not found at {filepath}")
+        return {}
+    except json.JSONDecodeError:
+        logger.error(f"Invalid JSON format in {filepath}")
+        return {}
+    except Exception as e:
+        logger.error(f"Unable to load JSON file: {str(e)}")
+        return {}
+
+
 def write_json(file_path, json_dict):
     """
     Writes a dictionary to a JSON file.
diff --git a/sequestrae_engine/db/__init__.py b/sequestrae_engine/db/__init__.py
new file mode 100644
index 0000000..2a14e53
--- /dev/null
+++ b/sequestrae_engine/db/__init__.py
@@ -0,0 +1,3 @@
+from sequestrae_engine.db.client import SupabaseClient
+
+__all__ = ["SupabaseClient"]
diff --git a/sequestrae_engine/db/client.py b/sequestrae_engine/db/client.py
new file mode 100644
index 0000000..d6aa315
--- /dev/null
+++ b/sequestrae_engine/db/client.py
@@ -0,0 +1,20 @@
+import os
+from typing import Optional
+
+from supabase import Client, create_client
+
+
+class SupabaseClient:
+    _instance: Optional[Client] = None
+
+    @classmethod
+    def get_client(cls, project_url: str = None, api_key: str = None) -> Client:
+        if cls._instance is None:
+            project_url = project_url or os.getenv("SUPABASE_PROJECT_URL")
+            api_key = api_key or os.getenv("SUPABASE_API_KEY")
+
+            if not project_url or not api_key:
+                raise ValueError("Missing Supabase credentials")
+
+            cls._instance = create_client(project_url, api_key)
+        return cls._instance
diff --git a/sequestrae_engine/db/scripts/populate_audit_reports.py b/sequestrae_engine/db/scripts/populate_audit_reports.py
new file mode 100644
index 0000000..cf1e19c
--- /dev/null
+++ b/sequestrae_engine/db/scripts/populate_audit_reports.py
@@ -0,0 +1,166 @@
+import json
+import logging
+from pathlib import Path
+from typing import Any, Dict, List
+
+from sequestrae_engine.core.utilities import get_json_files, load_json_file
+from sequestrae_engine.db.client import SupabaseClient
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+def populate_audit_reports(project_folder_path: str, client: SupabaseClient):
+    """Populate the audit_report_info table with JSON data."""
+    try:
+        json_path_list = get_json_files(project_folder_path)
+        logger.info(f"Found {len(json_path_list)} JSON files")
+
+        json_list = []
+        for json_path in json_path_list:
+            try:
+                data = load_json_file(json_path)
+                json_list.append(data)
+                logger.info(f"Loaded {json_path}")
+            except Exception as e:
+                logger.error(f"Error loading {json_path}: {e}")
+                continue
+
+        if not json_list:
+            raise ValueError("No valid JSON files found to insert")
+
+        response = client.table("audit_report_info").insert(json_list).execute()
+
+        logger.info(f"Successfully inserted {len(json_list)} records")
+
+    except Exception as e:
+        logger.error(f"Failed to populate audit reports: {e}")
+        raise e
+
+
+def populate_feedstock_evaluation_table(project_folder_path: str, client: SupabaseClient):
+    """Populate the feedstock_evaluation table with JSON data."""
+    try:
+        json_path_list = get_json_files(project_folder_path)
+        feestock_analysis_json_list = [
+            json_path for json_path in json_path_list if "feedstock_analysis" in json_path
+        ]
+        logger.info(f"Found {len(feestock_analysis_json_list)} feestock analysis JSON files")
+
+        json_list = []
+        for json_path in feestock_analysis_json_list:
+            try:
+                list_of_dict = load_json_file(json_path)
+
+                # get the file name but remove the extension `feedstock_analysis` and replace the .json by .md
+                audit_file = Path(json_path).stem.replace("_feedstock_analysis", "") + ".md"
+
+                for dct in list_of_dict:
+                    analysis_content = dct.get("analysis")
+                    for criteria_item in analysis_content:
+                        criteria_item["audit_file"] = audit_file
+                        # TODO: temporary fix to rename the keys, we should update the prompts instead
+                        criteria_item["missing_or_contradictory_data"] = criteria_item.pop(
+                            "missingOrContradictoryData"
+                        )
+                        json_list.append(criteria_item)
+
+                logger.info(f"Loaded {json_path}")
+            except Exception as e:
+                logger.error(f"Error loading {json_path}: {e}")
+                continue
+
+        if not json_list:
+            raise ValueError("No valid JSON files found to insert")
+
+        response = client.table("feestock_evaluation").insert(json_list).execute()
+
+        logger.info(f"Successfully inserted {len(json_list)} records")
+
+    except Exception as e:
+        logger.error(f"Failed to populate feedstock evaluation: {e}")
+        raise e
+
+
+def populate_project_analysis_table(meta_folder_path: str, registry: str, client: SupabaseClient):
+    """Populate the project_analysis table with due diligence analysis data."""
+    # Define expected schema fields
+    required_fields = {
+        "topic",
+        "sub_topic",
+        "risk_factor",
+        "question",
+        "short_answer",
+        "long_answer",
+        "detail_level",
+        "missing_data",
+        "contradictory_data",
+        "is_hallucination",
+        "hallucination_reasoning",
+    }
+
+    try:
+        # Get all project folders
+        project_folders = [f for f in Path(meta_folder_path).iterdir() if f.is_dir()]
+        logger.info(f"Found {len(project_folders)} project folders")
+
+        all_records = []
+        for project_folder in project_folders:
+            try:
+                project_name = project_folder.name.replace(" ", "_")
+                analysis_file = (
+                    project_folder
+                    / "analysis"
+                    / f"{project_name}_analysis_mistral-large-latest.json"
+                )
+
+                if not analysis_file.exists():
+                    logger.warning(f"Analysis file not found for project: {project_name}")
+                    continue
+
+                analysis_data = load_json_file(analysis_file)
+
+                # Validate each record before adding
+                valid_records = []
+                has_invalid_records = False
+
+                for record in analysis_data:
+                    record_fields = set(record.keys())
+
+                    # Check for missing required fields
+                    missing_fields = required_fields - record_fields
+                    if missing_fields:
+                        has_invalid_records = True
+                        logger.warning(
+                            f"Project {project_name} has records with missing fields: {missing_fields}. "
+                            f"Skipping entire project for data consistency."
+                        )
+                        break
+
+                    # Ensure boolean type for is_hallucination
+                    record["is_hallucination"] = bool(record["is_hallucination"])
+
+                    # Add project metadata
+                    record["project_name"] = project_name
+                    record["registry"] = registry
+                    valid_records.append(record)
+
+                if not has_invalid_records and valid_records:
+                    all_records.extend(valid_records)
+                    logger.info(f"Processed analysis for project: {project_name}")
+
+            except Exception as e:
+                logger.error(f"Error processing project folder {project_folder}: {e}")
+                continue
+
+        if not all_records:
+            raise ValueError("No valid analysis records found to insert")
+
+        response = client.table("project_analysis").insert(all_records).execute()
+        logger.info(f"Successfully inserted {len(all_records)} analysis records")
+
+    except Exception as e:
+        logger.error(f"Failed to populate project analysis: {e}")
+        raise e
diff --git a/sequestrae_engine/db/scripts/populate_registry_analysis.py b/sequestrae_engine/db/scripts/populate_registry_analysis.py
new file mode 100644
index 0000000..68ddcbe
--- /dev/null
+++ b/sequestrae_engine/db/scripts/populate_registry_analysis.py
@@ -0,0 +1,353 @@
+import logging
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import pandas as pd
+
+from sequestrae_engine.core.utilities import load_json_file
+from sequestrae_engine.db.client import SupabaseClient
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+def populate_registry_analysis(registries_dir: str, client: SupabaseClient):
+    """
+    Parse multiple registry folders and upload project analysis data to Supabase tables.
+
+    Args:
+        registries_dir: Path to the root folder containing multiple registry folders
+        client: SupabaseClient instance for database operations
+
+    Returns:
+        Dictionary mapping table names to their inserted content lists
+    """
+    try:
+        registries_path = Path(registries_dir)
+        if not registries_path.exists() or not registries_path.is_dir():
+            raise ValueError(f"Directory not found or is not a directory: {registries_path}")
+
+        # Find all registry folders (direct subdirectories of the root)
+        registry_folders = [f for f in registries_path.iterdir() if f.is_dir()]
+
+        if not registry_folders:
+            raise ValueError(f"No registry folders found in {registries_path}")
+
+        logger.info(f"Found {len(registry_folders)} registry folders to process")
+
+        # Initialize IDs and table content lists
+        project_id = 1
+        detailed_answer_id = 1
+        subtopic_summary_id = 1
+        risk_factor_id = 1
+        risk_factor_point_id = 1
+        topic_summary_id = 1
+
+        projects_table_content_list = []
+        main_insight_content_list = []
+        detailed_answers_table_content_list = []
+        subtopic_summaries_table_content_list = []
+        risk_factors_table_content_list = []
+        risk_factor_points_table_content_list = []
+        topic_summaries_table_content_list = []
+
+        # Process each registry folder
+        for registry_folder in registry_folders:
+            registry_name = registry_folder.name
+            logger.info(f"Processing registry: {registry_name}")
+
+            try:
+                # Find all project folders within the registry directory
+                project_folders = [f for f in registry_folder.iterdir() if f.is_dir()]
+
+                if not project_folders:
+                    logger.warning(
+                        f"No project folders found in registry: {registry_name}, skipping..."
+                    )
+                    continue
+
+                logger.info(
+                    f"Found {len(project_folders)} project folders in registry '{registry_name}'"
+                )
+
+                # Process each project folder
+                for project_folder in project_folders:
+                    try:
+                        project_name = project_folder.name.replace(" ", "_")
+                        logger.info(f"Processing project: {project_name}")
+
+                        # Define expected analysis file names
+                        project_metadata = "metadata.json"
+
+                        project_overview_filename = (
+                            f"{project_name}_overview_mistral-large-latest.json"
+                        )
+                        project_main_insights_filename = (
+                            f"{project_name}_analysis_mistral-large-latest_main_insights.json"
+                        )
+
+                        detailed_answers_filename = (
+                            f"{project_name}_analysis_mistral-large-latest.json"
+                        )
+                        subtopic_summaries_filename = (
+                            f"{project_name}_analysis_mistral-large-latest_subtopic_summaries.json"
+                        )
+                        topic_summaries_filename = f"{project_name}_analysis_mistral-large-latest_subtopic_summaries_topic_summaries.json"
+
+                        # Find analysis files
+                        analysis_folder = project_folder / "analysis"
+                        if not analysis_folder.exists() or not analysis_folder.is_dir():
+                            logger.warning(f"Analysis folder not found for project: {project_name}")
+                            continue
+
+                        analysis_files = [f for f in analysis_folder.iterdir()]
+                        filename_to_path = {path.name: path for path in analysis_files}
+
+                        # Verify all required files exist
+                        file_path_dict = {}
+                        required_files = [
+                            project_metadata,
+                            project_overview_filename,
+                            project_main_insights_filename,
+                            detailed_answers_filename,
+                            subtopic_summaries_filename,
+                            topic_summaries_filename,
+                        ]
+
+                        missing_files = []
+                        for filename in required_files:
+                            if filename in filename_to_path:
+                                file_path_dict[filename] = filename_to_path[filename]
+                            else:
+                                missing_files.append(filename)
+
+                        if missing_files:
+                            logger.warning(
+                                f"Missing required analysis files for project {project_name}: {missing_files}"
+                            )
+                            continue
+
+                        # Add project to projects table
+                        project_metadata = load_json_file(file_path_dict.get(project_metadata))
+
+                        project_overview_data = load_json_file(
+                            file_path_dict.get(project_overview_filename)
+                        )
+
+                        projects_table_content_list.append(
+                            {
+                                "project_id": project_id,
+                                "project_name": project_name,
+                                "registry": registry_name,
+                                "project_url": project_metadata.get("project_url"),
+                                "project_description": project_overview_data.get(
+                                    "project_description"
+                                ),
+                                "country": project_overview_data.get("country"),
+                                "project_start_period": project_overview_data.get(
+                                    "project_start_period"
+                                ),
+                                "feedstock_type": project_overview_data.get("feedstock_type"),
+                                "feedstock_type_in_string": ", ".join(
+                                    project_overview_data.get("feedstock_type")
+                                ),
+                                "key_stakeholders": project_overview_data.get("key_stakeholders"),
+                            }
+                        )
+
+                        project_main_insights = load_json_file(
+                            file_path_dict.get(project_main_insights_filename)
+                        )
+
+                        for main_insight_type in [
+                            "main_strengths",
+                            "main_considerations",
+                            "main_recommended_actions",
+                        ]:
+                            for insight_dict in project_main_insights.get(main_insight_type, []):
+                                main_insight_dict = {
+                                    "project_id": project_id,
+                                    "insight_type": main_insight_type,
+                                    "topic": insight_dict.get("topic"),
+                                    "main_idea": insight_dict.get("main_idea"),
+                                }
+                                main_insight_content_list.append(main_insight_dict)
+
+                        # Process detailed answers
+                        try:
+                            detailed_answers = load_json_file(
+                                file_path_dict.get(detailed_answers_filename)
+                            )
+                            for detailed_answer in detailed_answers:
+                                detailed_answer_dict = {
+                                    "detailed_answer_id": detailed_answer_id,
+                                    "project_id": project_id,
+                                    "topic": detailed_answer.get("topic"),
+                                    "sub_topic": detailed_answer.get("sub_topic"),
+                                    "risk_factor": detailed_answer.get("risk_factor"),
+                                    "question": detailed_answer.get("question"),
+                                    "short_answer": detailed_answer.get("short_answer"),
+                                    "long_answer": detailed_answer.get("long_answer"),
+                                    "detail_level": detailed_answer.get("detail_level"),
+                                    "missing_data": detailed_answer.get("missing_data"),
+                                    "contradictory_data": detailed_answer.get("contradictory_data"),
+                                    "is_hallucination": (
+                                        None
+                                        if detailed_answer.get("is_hallucination") == ""
+                                        else bool(detailed_answer.get("is_hallucination"))
+                                    ),
+                                    "hallucination_reasoning": detailed_answer.get(
+                                        "hallucination_reasoning"
+                                    ),
+                                }
+                                detailed_answers_table_content_list.append(detailed_answer_dict)
+                                detailed_answer_id += 1
+                        except Exception as e:
+                            logger.error(
+                                f"Error processing detailed answers for {project_name}: {e}"
+                            )
+
+                        # Process subtopic summaries
+                        try:
+                            subtopic_summaries = load_json_file(
+                                file_path_dict.get(subtopic_summaries_filename)
+                            )
+                            for subtopic_summary in subtopic_summaries:
+                                subtopic_summary_dict = {
+                                    "subtopic_summary_id": subtopic_summary_id,
+                                    "project_id": project_id,
+                                    "topic": subtopic_summary.get("topic"),
+                                    "sub_topic": subtopic_summary.get("sub_topic"),
+                                    "overall_summary": subtopic_summary.get("overall_summary"),
+                                }
+                                subtopic_summaries_table_content_list.append(subtopic_summary_dict)
+
+                                # Process risk factors within each subtopic
+                                for risk_factor in subtopic_summary.get("analysis", []):
+                                    risk_factor_dict = {
+                                        "risk_factor_id": risk_factor_id,
+                                        "subtopic_summary_id": subtopic_summary_id,
+                                        "risk_factor_name": risk_factor.get("risk_factor"),
+                                    }
+                                    risk_factors_table_content_list.append(risk_factor_dict)
+
+                                    # Process points for each risk factor
+                                    for point_type in [
+                                        "strengths",
+                                        "considerations",
+                                        "recommended_actions",
+                                    ]:
+                                        for point_info in risk_factor.get(point_type, []):
+                                            risk_factor_points_dict = {
+                                                "risk_factor_point_id": risk_factor_point_id,
+                                                "risk_factor_id": risk_factor_id,
+                                                "point_type": point_type,
+                                            }
+
+                                            # Handle different structures based on point type
+                                            if point_type == "recommended_actions":
+                                                risk_factor_points_dict["main_idea"] = (
+                                                    point_info.get("action")
+                                                )
+                                            else:
+                                                risk_factor_points_dict["main_idea"] = (
+                                                    point_info.get("main_idea")
+                                                )
+
+                                            risk_factor_points_dict["explanation"] = point_info.get(
+                                                "explanation"
+                                            )
+                                            risk_factor_points_table_content_list.append(
+                                                risk_factor_points_dict
+                                            )
+                                            risk_factor_point_id += 1
+
+                                    risk_factor_id += 1
+                                subtopic_summary_id += 1
+                        except Exception as e:
+                            logger.error(
+                                f"Error processing subtopic summaries for {project_name}: {e}"
+                            )
+
+                        # Process topic summaries
+                        try:
+                            topic_summaries = load_json_file(
+                                file_path_dict.get(topic_summaries_filename)
+                            )
+                            for topic_summary in topic_summaries:
+                                topic_summary_dict = {
+                                    "topic_summary_id": topic_summary_id,
+                                    "project_id": project_id,
+                                    "topic": topic_summary.get("topic"),
+                                    "topic_introduction": topic_summary.get("topic_introduction"),
+                                    "topic_summary": topic_summary.get("topic_summary"),
+                                }
+                                topic_summaries_table_content_list.append(topic_summary_dict)
+                                topic_summary_id += 1
+                        except Exception as e:
+                            logger.error(
+                                f"Error processing topic summaries for {project_name}: {e}"
+                            )
+
+                        logger.info(f"Successfully processed project: {project_name}")
+                        project_id += 1
+
+                    except Exception as e:
+                        logger.error(f"Error processing project folder {project_folder}: {e}")
+                        continue
+
+            except Exception as e:
+                logger.error(f"Error processing registry {registry_name}: {str(e)}")
+                continue
+
+        # Map table names to their content
+        table_data_mapping = {
+            "projects": projects_table_content_list,
+            "project_main_insights": main_insight_content_list,
+            "detailed_answers": detailed_answers_table_content_list,
+            "subtopic_summaries": subtopic_summaries_table_content_list,
+            "risk_factors": risk_factors_table_content_list,
+            "risk_factor_points": risk_factor_points_table_content_list,
+            "topic_summaries": topic_summaries_table_content_list,
+        }
+
+        # Insert data into Supabase tables (single API call per table)
+        for table_name, table_content in table_data_mapping.items():
+            if not table_content:
+                logger.warning(f"No data to insert for table: {table_name}")
+                continue
+
+            logger.info(f"Inserting {len(table_content)} total records into {table_name} table")
+            response = client.table(table_name).insert(table_content).execute()
+            logger.info(f"Successfully inserted data into {table_name} table")
+
+        return table_data_mapping
+
+    except Exception as e:
+        logger.error(f"Failed to populate registry analysis data: {e}")
+        raise e
+
+
+def main():
+    """Example usage of the populate_registry_analysis function."""
+    import os
+
+    from dotenv import load_dotenv
+
+    # Load environment variables
+    load_dotenv()
+
+    # Initialize Supabase client
+    client = SupabaseClient.get_client()
+
+    # Example usage
+    registries_dir = "../../project_data/"
+
+    # Populate database tables
+    populate_registry_analysis(registries_dir, client)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sequestrae_engine/document_parsing/__init__.py b/sequestrae_engine/document_parsing/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/sequestrae_engine/document_parsing/extractors.py b/sequestrae_engine/document_parsing/extractors.py
new file mode 100644
index 0000000..6c20cda
--- /dev/null
+++ b/sequestrae_engine/document_parsing/extractors.py
@@ -0,0 +1,1287 @@
+import json
+import logging
+import os
+import re
+import time
+from typing import Any, Dict, List, Literal, Optional, Union
+
+import pandas as pd
+from mistralai import Mistral
+
+from sequestrae_engine.core.utilities import load_json_file
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# Define paths relative to this file
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_OVERVIEW_EXTRACTION_PROMPT_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/project_overview_extraction_prompt.txt"
+)
+# Define prompt paths that have current/future versions
+DUE_DILIGENCE_SYSTEM_PROMPT_CURRENT_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/due_diligence_system_prompt_current_project.txt"
+)
+DUE_DILIGENCE_SYSTEM_PROMPT_FUTURE_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/due_diligence_system_prompt_future_project.txt"
+)
+DUE_DILIGENCE_CRITERIA_PATH = os.path.join(SCRIPT_DIR, "prompts/due_diligence_criteria.csv")
+
+HALLUCINATION_DETECTION_PROMPT_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/hallucination_evaluation_prompt.txt"
+)
+
+HALLUCINATION_FIXING_PROMPT_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/hallucination_fixing_prompt.txt"
+)
+
+SUBTOPIC_RESUME_CURRENT_PROMPT_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/subtopic_resume_prompt_current_project.txt"
+)
+SUBTOPIC_RESUME_FUTURE_PROMPT_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/subtopic_resume_prompt_future_project.txt"
+)
+SUBTOPIC_REFINE_PROMPT_PATH = os.path.join(SCRIPT_DIR, "prompts/subtopic_refine_prompt.txt")
+
+TOPIC_RESUME_CURRENT_PROMPT_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/topic_resume_prompt_current_project.txt"
+)
+TOPIC_RESUME_FUTURE_PROMPT_PATH = os.path.join(
+    SCRIPT_DIR, "prompts/topic_resume_prompt_future_project.txt"
+)
+
+PROJECT_MAIN_IDEA_PROMPT_PATH = os.path.join(SCRIPT_DIR, "prompts/project_main_insight_prompt.txt")
+
+MODEL_TEMPERATURE = 0
+
+REQUIRED_FIELDS_FOR_DUE_DILIGENCE = {
+    "topic",
+    "sub_topic",
+    "risk_factor",
+    "question",
+    "short_answer",
+    "long_answer",
+    "detail_level",
+    "missing_data",
+    "contradictory_data",
+}
+
+REQUIRED_FIELDS_FOR_SUBTOPIC_SUMMARIES = {"topic", "sub_topic", "analysis"}
+
+REQUIRED_FIELDS_FOR_TOPIC_SUMMARIES = {"topic", "topic_summary"}
+
+REQUIRED_FIELDS_FOR_MAIN_INSIGHTS = {
+    "main_strengths",
+    "main_considerations",
+    "main_recommended_actions",
+}
+
+
+def read_markdown_file(filepath: str) -> str:
+    """
+    Read a markdown file and return its contents as a string
+
+    Args:
+        filepath (str): Path to the markdown file
+
+    Returns:
+        str: Contents of the markdown file
+    """
+    try:
+        with open(filepath, "r", encoding="utf-8") as file:
+            content = file.read()
+        return content
+    except FileNotFoundError:
+        raise FileNotFoundError(f"Could not find markdown file at {filepath}")
+    except Exception as e:
+        raise Exception(f"Error reading markdown file: {str(e)}")
+
+
+class PathManager:
+    """
+    Manages file paths for project data, standardizing path creation and derivation.
+    """
+
+    # Analysis file type constants
+    ANALYSIS = "analysis"
+    OVERVIEW = "overview"
+    SUBTOPIC_SUMMARIES = "subtopic_summaries"
+    TOPIC_SUMMARIES = "topic_summaries"
+    MAIN_INSIGHTS = "main_insights"
+
+    # Directory structure constants
+    ANALYSIS_DIR = "analysis"
+    PARSED_MARKDOWN_DIR = "parsed_markdown"
+
+    # Common file names
+    DEFAULT_MARKDOWN_FILE = "concatenated_documentation.md"
+
+    def __init__(self, model: str, project_folder: str):
+        """
+        Initialize the PathManager with a model name and project folder.
+
+        Args:
+            model (str): Model name used for file naming
+            project_folder (str): Path to project folder
+        """
+        self.model = model
+        self.project_folder = project_folder
+        # Always infer project name from project folder
+        self._project_name = os.path.basename(os.path.normpath(project_folder))
+
+    def set_project_folder(self, project_folder: str) -> None:
+        """
+        Set or update the project folder path.
+
+        Args:
+            project_folder (str): Path to the project folder
+        """
+        self.project_folder = project_folder
+        # Update project name when folder changes
+        self._project_name = os.path.basename(os.path.normpath(project_folder))
+
+    @property
+    def project_name(self) -> str:
+        """
+        Get project name from the project folder path.
+
+        Returns:
+            str: Name of the project derived from folder path
+        """
+        return self._project_name
+
+    def get_analysis_dir(self) -> str:
+        """
+        Get the standard analysis directory path.
+
+        Returns:
+            str: Path to the analysis directory
+        """
+        return os.path.join(self.project_folder, self.ANALYSIS_DIR)
+
+    def get_markdown_dir(self) -> str:
+        """
+        Get the standard parsed markdown directory path.
+
+        Returns:
+            str: Path to the parsed markdown directory
+        """
+        return os.path.join(self.project_folder, self.PARSED_MARKDOWN_DIR)
+
+    def get_markdown_path(self, filename: Optional[str] = None) -> str:
+        """
+        Get path to a markdown file in the standard location.
+
+        Args:
+            filename (Optional[str]): Filename of the markdown file, defaults to standard name
+
+        Returns:
+            str: Path to the markdown file
+        """
+        if not filename:
+            filename = self.DEFAULT_MARKDOWN_FILE
+        return os.path.join(self.get_markdown_dir(), filename)
+
+    def get_analysis_file_path(self) -> str:
+        """
+        Get path to the analysis file in the standard location.
+
+        Returns:
+            str: Path to the analysis file
+        """
+        filename = f"{self.project_name}_{self.ANALYSIS}.json"
+        return os.path.join(self.get_analysis_dir(), filename)
+
+    def get_overview_file_path(self) -> str:
+        """
+        Get path to the overview file in the standard location.
+
+        Returns:
+            str: Path to the overview file
+        """
+        filename = f"{self.project_name}_{self.OVERVIEW}.json"
+        return os.path.join(self.get_analysis_dir(), filename)
+
+    def get_subtopic_summaries_path(self) -> str:
+        """
+        Get path to the subtopic summaries file in the standard location.
+
+        Returns:
+            str: Path to the subtopic summaries file
+        """
+        return os.path.join(
+            self.get_analysis_dir(), f"{self.project_name}_{self.SUBTOPIC_SUMMARIES}.json"
+        )
+
+    def get_topic_summaries_path(self) -> str:
+        """
+        Get path to the topic summaries file in the standard location.
+
+        Returns:
+            str: Path to the topic summaries file
+        """
+        return os.path.join(
+            self.get_analysis_dir(), f"{self.project_name}_{self.TOPIC_SUMMARIES}.json"
+        )
+
+    def get_main_insights_path(self) -> str:
+        """
+        Get path to the main insights file in the standard location.
+
+        Returns:
+            str: Path to the main insights file
+        """
+        return os.path.join(
+            self.get_analysis_dir(), f"{self.project_name}_{self.MAIN_INSIGHTS}.json"
+        )
+
+    def get_concatenated_doc_path(self) -> str:
+        """
+        Get the full path to the concatenated documentation file.
+
+        Returns:
+            str: Full path to the concatenated documentation file
+        """
+        filename = f"{self.project_name}_concatenated_documentation.md"
+        return self.get_markdown_path(filename)
+
+    def get_analysis_checkpoint_path(self) -> str:
+        """
+        Get path to the analysis checkpoint file in the standard location.
+
+        Returns:
+            str: Path to the analysis checkpoint file
+        """
+        filename = f"{self.project_name}_{self.ANALYSIS}_checkpoint.json"
+        return os.path.join(self.get_analysis_dir(), filename)
+
+
+class AuditReportExtractor:
+    def __init__(
+        self,
+        mistral_api_key: str,
+        project_folder: str,
+        model: str = "mistral-large-latest",
+        overwrite: bool = False,
+    ):
+        """
+        Initialize the AuditReportExtractor with common parameters.
+
+        Args:
+            mistral_api_key (str): API key for Mistral
+            model (str): Model to use for LLM inference
+            project_folder (Optional[str]): Path to the project folder
+            project_type (str): Type of project - "current" (ongoing) or "future" (planned)
+            overwrite (bool): Whether to overwrite existing output files
+        """
+        self.model = model
+        self.mistral_client = Mistral(api_key=mistral_api_key)
+        self.overwrite = overwrite
+        self.path_manager = PathManager(model, project_folder)
+        self.project_type = None
+
+    def set_project_folder(self, project_folder: str) -> None:
+        """
+        Set the project folder for analysis.
+
+        Args:
+            project_folder (str): Path to the project folder
+        """
+        self.path_manager.set_project_folder(project_folder)
+
+    def _get_prompt_path(self, current_prompt_path: str, future_prompt_path: str) -> str:
+        """
+        Helper method to get the appropriate prompt path based on project type.
+
+        Args:
+            current_prompt_path (str): Path to the prompt file for current projects
+            future_prompt_path (str): Path to the prompt file for future projects
+
+        Returns:
+            str: Selected prompt path
+        """
+        return current_prompt_path if self.project_type == "current" else future_prompt_path
+
+    def _should_skip_existing(self, output_path: str) -> bool:
+        """
+        Check if output file exists and should be skipped.
+
+        Args:
+            output_path (str): Path to the output file
+
+        Returns:
+            bool: True if file exists and should be skipped, False otherwise
+        """
+        if os.path.exists(output_path) and not self.overwrite:
+            logger.info(
+                f"Output file already exists at {output_path} and overwrite=False. Skipping."
+            )
+            return True
+        return False
+
+    def _ensure_output_directory(self, output_path: str) -> None:
+        """
+        Ensure the output directory exists.
+
+        Args:
+            output_path (str): Path to the output file
+        """
+        output_dir = os.path.dirname(output_path)
+        if output_dir:  # Only create directory if path has a parent directory
+            os.makedirs(output_dir, exist_ok=True)
+
+    def _save_json_output(self, data: Union[Dict, List], output_path: str) -> None:
+        """
+        Save data to JSON file.
+
+        Args:
+            data (Union[Dict, List]): Data to save
+            output_path (str): Path to the output file
+        """
+        self._ensure_output_directory(output_path)
+        with open(output_path, "w", encoding="utf-8") as f:
+            json.dump(data, f, indent=2)
+        logger.info(f"Data saved to {output_path}")
+
+    def extract_project_overview_data(self, overwrite: Optional[bool] = None):
+        """
+        Extract project overview data from the standard markdown document using Mistral LLM.
+
+        Args:
+            overwrite (Optional[bool]): Whether to overwrite existing output file
+
+        Returns:
+            Dict: Dictionary containing project overview data
+        """
+        # Use instance values if not specified
+        overwrite = self.overwrite if overwrite is None else overwrite
+
+        # Get standard file paths
+        markdown_document_path = self.path_manager.get_concatenated_doc_path()
+        output_path = self.path_manager.get_overview_file_path()
+
+        # Check if file exists and should be skipped
+        if self._should_skip_existing(output_path):
+            # Load and return existing file
+            return load_json_file(output_path)
+
+        # Read the markdown file
+        with open(markdown_document_path, "r", encoding="utf-8") as file:
+            report_content = file.read()
+
+        # Load the project overview extraction prompt
+        with open(PROJECT_OVERVIEW_EXTRACTION_PROMPT_PATH, "r") as file:
+            system_prompt = file.read()
+
+        user_message_template = """
+            Analyze carefully the following markdown and return the result in short JSON object:
+
+            ```markdown
+            {report_content}
+            ```
+        """
+
+        user_message = user_message_template.format(report_content=report_content)
+        full_message_template = """
+            {system_prompt}
+            ---
+            {user_prompt}
+        """
+
+        full_message = full_message_template.format(
+            system_prompt=system_prompt, user_prompt=user_message
+        )
+
+        messages = [
+            {
+                "role": "user",
+                "content": full_message,
+            }
+        ]
+
+        chat_response = self.mistral_client.chat.complete(
+            model=self.model,
+            messages=messages,
+            response_format={
+                "type": "json_object",
+            },
+            temperature=MODEL_TEMPERATURE,
+        )
+
+        response_content = chat_response.choices[0].message.content
+
+        # Define required fields for project overview (adjust as needed)
+        required_fields = {
+            "country",
+            "project_start_period",
+            "feedstock_type",
+            "project_description",
+            "key_stakeholders",
+        }
+
+        # Validate and fix the response
+        project_overview_data = self._validate_and_fix_llm_response(
+            response_content, required_fields
+        )
+
+        # Save the JSON file using the helper method
+        self._save_json_output(project_overview_data, output_path)
+
+        logger.info(
+            f"Extracted project overview data from {markdown_document_path} and saved to {output_path}"
+        )
+
+        return project_overview_data
+
+    def analyze_due_diligence_criteria(self, overwrite: Optional[bool] = None):
+        """
+        Analyze due diligence criteria for a project with checkpointing for fault tolerance.
+
+        Args:
+            overwrite (Optional[bool]): Whether to overwrite existing output
+        """
+        # Use instance values if not specified
+        overwrite = self.overwrite if overwrite is None else overwrite
+
+        # Get standard file paths
+        markdown_document_path = self.path_manager.get_concatenated_doc_path()
+        output_path = self.path_manager.get_analysis_file_path()
+        checkpoint_path = self.path_manager.get_analysis_checkpoint_path()
+
+        # Check for ambiguous state (both output and checkpoint exist)
+        if os.path.exists(output_path) and os.path.exists(checkpoint_path) and not overwrite:
+            logger.warning(f"Both output file and checkpoint file exist. Using the output file.")
+            # Prioritize the output file in this case
+            return
+
+        # Check if final output file exists and should be skipped
+        if self._should_skip_existing(output_path):
+            return
+
+        # Check if checkpoint file exists
+        checkpoint_exists = os.path.exists(checkpoint_path) and not overwrite
+        result_list = []
+        processed_indices = set()
+
+        if checkpoint_exists:
+            try:
+                with open(checkpoint_path, "r", encoding="utf-8") as f:
+                    checkpoint_data = json.load(f)
+                    result_list = checkpoint_data.get("results", [])
+                    processed_indices = set(map(int, checkpoint_data.get("processed_indices", [])))
+                    logger.info(
+                        f"Resuming from checkpoint with {len(result_list)} results and {len(processed_indices)} processed items"
+                    )
+            except (json.JSONDecodeError, FileNotFoundError) as e:
+                logger.warning(f"Error loading checkpoint file: {str(e)}. Starting from beginning.")
+                result_list = []
+                processed_indices = set()
+
+        # Select the appropriate prompt file based on project_type
+        due_diligence_prompt_path = self._get_prompt_path(
+            DUE_DILIGENCE_SYSTEM_PROMPT_CURRENT_PATH, DUE_DILIGENCE_SYSTEM_PROMPT_FUTURE_PATH
+        )
+
+        with open(due_diligence_prompt_path, "r") as f:
+            context_content = f.read()
+
+        criteria_df = pd.read_csv(DUE_DILIGENCE_CRITERIA_PATH)
+        grouped_criteria_df = (
+            criteria_df.groupby(["topic", "sub_topic", "risk_factor"])
+            .apply(lambda x: dict(zip(x["question"], x["instruction"])))
+            .reset_index(name="qa_pairs")
+        )
+
+        # Template for the full message
+        full_message_template = """
+        {context_content}
+
+        **Criteria Guideline**
+
+        Topic: {topic}
+        Subtopic: {sub_topic}
+        Risk factor: {risk_factor}
+        Questions & Instruction: {questions}
+
+        **Project documents**
+
+        {audit_report}
+        """
+
+        with open(markdown_document_path, "r") as file:
+            concatenated_project_doc = file.read()
+
+        total_count = len(grouped_criteria_df)
+        processed_count = len(processed_indices)
+        remaining_count = total_count - processed_count
+
+        # Ensure output directory exists
+        self._ensure_output_directory(checkpoint_path)
+
+        # Start time for this session
+        session_start_time = time.time()
+        # Track items processed in this current execution session
+        session_processed_count = 0
+
+        for idx, r in grouped_criteria_df.iterrows():
+            # Skip already processed items
+            if idx in processed_indices:
+                continue
+
+            # Calculate progress stats
+            processed_count += 1
+            remaining_count -= 1
+            session_processed_count += 1  # Track items processed in this session
+            percent_complete = (processed_count / total_count) * 100
+
+            # Estimate time remaining if we have processed at least a few items in this session
+            time_elapsed = time.time() - session_start_time
+
+            if session_processed_count > 0:
+                # Calculate average time per item based only on items processed in this session
+                avg_time_per_item = time_elapsed / session_processed_count
+                # Estimate remaining time based on items still to process
+                est_time_remaining_mins = (avg_time_per_item * remaining_count) / 60
+                # Format nicely with appropriate precision
+                if est_time_remaining_mins < 1:
+                    eta_str = f", ETA: <1 minute"
+                else:
+                    eta_str = f", ETA: {est_time_remaining_mins:.1f} minutes"
+            else:
+                eta_str = ""
+
+            print(
+                f"[{processed_count}/{total_count}] {percent_complete:.1f}%{eta_str} - "
+                f"Processing: {r['topic']}, {r['sub_topic']}, {r['risk_factor']} "
+                f"({len(r['qa_pairs'])} questions)"
+            )
+
+            start_time = time.time()
+            question_list = []
+            template_instruction = """
+                Question: {question}.
+                Instruction: {instruction}
+                ----
+            """
+
+            for question, instruction in r["qa_pairs"].items():
+                question_list.append(
+                    template_instruction.format(question=question, instruction=instruction)
+                )
+
+            joined_question = "".join(question_list)
+
+            full_message = full_message_template.format(
+                context_content=context_content,
+                topic=r["topic"],
+                sub_topic=r["sub_topic"],
+                risk_factor=r["risk_factor"],
+                questions=joined_question,
+                audit_report=concatenated_project_doc,
+            )
+            messages = [{"role": "user", "content": full_message}]
+
+            try:
+                chat_response = self.mistral_client.chat.complete(
+                    model="mistral-large-latest",
+                    messages=messages,
+                    response_format={"type": "json_object"},
+                    temperature=MODEL_TEMPERATURE,
+                )
+
+                response_content = chat_response.choices[0].message.content
+                due_diligence_json_array = self._validate_and_fix_llm_response(
+                    response_content, REQUIRED_FIELDS_FOR_DUE_DILIGENCE
+                )
+
+                result_with_hallucination_analysis = self._analyze_hallucination(
+                    due_diligence_json_array, concatenated_project_doc
+                )
+
+                fixed_results = self._fix_hallucinations_recursive(
+                    result_with_hallucination_analysis, concatenated_project_doc
+                )
+                result_list.extend(fixed_results)
+
+                # Mark as processed and update checkpoint file
+                processed_indices.add(idx)
+
+                # Save checkpoint
+                checkpoint_data = {
+                    "results": result_list,
+                    "processed_indices": list(processed_indices),
+                    "last_updated": time.strftime("%Y-%m-%d %H:%M:%S"),
+                    "progress_percent": percent_complete,
+                }
+                with open(checkpoint_path, "w", encoding="utf-8") as f:
+                    json.dump(checkpoint_data, f, indent=2)
+
+                running_time_in_minutes = round((time.time() - start_time) / 60, 2)
+                print(f"    Item completed in {running_time_in_minutes} minutes")
+                time.sleep(1)  # Rate limiting
+
+            except Exception as e:
+                logger.error(f"Error processing criteria {idx}: {str(e)}")
+                logger.info(
+                    f"Progress saved in checkpoint file. You can resume by running the function again."
+                )
+                # Return early but don't raise the exception - allows resuming from this point
+                return
+
+        # Save the final results using the helper method
+        self._save_json_output(result_list, output_path)
+
+        # If successful, clean up the checkpoint file
+        if os.path.exists(checkpoint_path) and os.path.exists(output_path):
+            try:
+                os.remove(checkpoint_path)
+                logger.info("Checkpoint file removed after successful completion")
+            except OSError:
+                logger.warning("Could not remove checkpoint file")
+
+        logger.info(f"Due diligence analysis completed with {len(result_list)} results")
+
+    def _validate_json_schema(self, json_data: List[Dict], required_fields: set) -> bool:
+        """
+        Validate that the input contains all required fields, handling both single dict and list of dicts.
+
+        Args:
+            json_data: Either a dictionary or list of dictionaries to validate
+            required_fields: Set of field names that must be present
+
+        Returns:
+            bool: True if valid, False if invalid
+        """
+        # Handle single dictionary
+        if isinstance(json_data, dict):
+            return all(field in json_data for field in required_fields)
+
+        # Handle list of dictionaries
+        if isinstance(json_data, list):
+            if not json_data:  # Empty list
+                return False
+            return all(
+                isinstance(item, dict) and all(field in item for field in required_fields)
+                for item in json_data
+            )
+
+        return False
+
+    def _analyze_hallucination(
+        self, criteria_response_json_array: List[Dict], concatenated_project_doc: str
+    ):
+        """
+        Analyze hallucination in the project document using Mistral LLM.
+
+        Args:
+            criteria_response: Dictionary containing the criteria response
+            concatenated_project_doc: Concatenated project document
+        """
+        start_time = time.time()
+
+        full_message_template = """
+        {context_content}
+
+        **Context**
+        The following text is the concatenated Markdown document to be used as context:
+        ```md
+        {concatenated_project_doc}
+        ```
+
+        **Answer to evaluate**
+        The following json object contains the answer provided by the previous LLM to be evaluated:
+        {criteria_response}
+        """
+        with open(HALLUCINATION_DETECTION_PROMPT_PATH, "r") as f:
+            context_content = f.read()
+
+        full_message = full_message_template.format(
+            context_content=context_content,
+            concatenated_project_doc=concatenated_project_doc,
+            criteria_response=criteria_response_json_array,
+        )
+
+        chat_response = self.mistral_client.chat.complete(
+            model="mistral-large-latest",
+            messages=[{"role": "user", "content": full_message}],
+            response_format={"type": "json_object"},
+            temperature=MODEL_TEMPERATURE,
+        )
+
+        response_content = chat_response.choices[0].message.content
+        response_json_array = self._validate_and_fix_llm_response(
+            response_content, REQUIRED_FIELDS_FOR_DUE_DILIGENCE
+        )
+        end_time = time.time()
+        running_time_in_seconds = round(end_time - start_time, 2)
+        # logger.info('Hallucination analysis complete in {} seconds'.format(running_time_in_seconds))
+        time.sleep(1)  # Rate limiting
+        return response_json_array
+
+    def _fix_hallucination(
+        self, criteria_response_json_array: List[Dict], concatenated_project_doc: str
+    ):
+        """
+        Fix hallucination in the project document using Mistral LLM.
+
+        Args:
+            criteria_response: Dictionary containing the criteria response
+            concatenated_project_doc: Concatenated project document
+        """
+        start_time = time.time()
+
+        full_message_template = """
+        {context_content}
+
+        **Context**
+        The following text is the concatenated Markdown document to be used as context:
+        ```md
+        {concatenated_project_doc}
+        ```
+
+        **Answer to fix**
+        The following json object contains the answer provided by the previous LLM with hallucination issues to be fixed:
+
+        ```json
+        {criteria_response}
+        ```
+
+        Return only the fixed JSON array and nothing else:
+
+        """
+        with open(HALLUCINATION_FIXING_PROMPT_PATH, "r") as f:
+            context_content = f.read()
+
+        full_message = full_message_template.format(
+            context_content=context_content,
+            concatenated_project_doc=concatenated_project_doc,
+            criteria_response=json.dumps(criteria_response_json_array, indent=2),
+        )
+
+        chat_response = self.mistral_client.chat.complete(
+            model="mistral-large-latest",
+            messages=[{"role": "user", "content": full_message}],
+            response_format={"type": "json_object"},
+            temperature=MODEL_TEMPERATURE,
+        )
+
+        response_content = chat_response.choices[0].message.content
+        response_json_array = self._validate_and_fix_llm_response(
+            response_content, REQUIRED_FIELDS_FOR_DUE_DILIGENCE
+        )
+        end_time = time.time()
+        running_time_in_seconds = round(end_time - start_time, 2)
+        time.sleep(1)
+        return response_json_array
+
+    def _fix_hallucinations_recursive(
+        self, result_json_array: List[Dict], concatenated_project_doc: str, max_iterations: int = 5
+    ) -> List[Dict]:
+        """
+        Recursively fix hallucinations in the results until no hallucinations are found or max iterations reached.
+        """
+        start_time = time.time()
+        iteration = 0
+
+        # Count initial hallucinations
+        initial_hallucinations = sum(
+            1 for result in result_json_array if result.get("is_hallucination") == True
+        )
+
+        if initial_hallucinations == 0:
+            # print("    No hallucinations detected in initial results")
+            return result_json_array
+
+        print(
+            f"    Starting hallucination fixing process. Found {initial_hallucinations} hallucinations"
+        )
+
+        while iteration < max_iterations:
+            fixed_result = self._fix_hallucination(result_json_array, concatenated_project_doc)
+            print("    fixed_result:", fixed_result)
+            print("------------")
+            analyzed_result = self._analyze_hallucination(fixed_result, concatenated_project_doc)
+            print("    analyzed_result:", analyzed_result)
+            print("------------")
+
+            num_remaining_hallucinations = sum(
+                1 for result in analyzed_result if result.get("is_hallucination") == True
+            )
+
+            if num_remaining_hallucinations == 0:
+                end_time = time.time()
+                running_time_in_minutes = round((end_time - start_time) / 60, 2)
+                print(
+                    f"    Successfully fixed {initial_hallucinations} hallucinations in {running_time_in_minutes} minutes after {iteration + 1} iterations"
+                )
+                return analyzed_result
+            else:
+                result_json_array = analyzed_result
+                iteration += 1
+                # logger.debug(f"Iteration {iteration + 1}: {current_hallucinations} remaining")
+                print(
+                    f"   Iteration {iteration + 1}: {num_remaining_hallucinations} hallucinations remaining"
+                )
+
+        logger.warning(
+            f"Reached maximum iterations ({max_iterations}). {num_remaining_hallucinations} remaining hallucinations"
+        )
+        return result_json_array
+
+    def _fix_malformatted_json(
+        self, criteria_response_json_array: List[Dict], required_fields: set
+    ):
+        """
+        Fix malformatted JSON array with LLM
+
+        Args:
+            criteria_response_json_array: Dictionary containing the criteria response
+            required_fields: Set of field names that must be present in each object
+        """
+
+        fields_list = "\n".join(f'* "{field}"' for field in required_fields)
+
+        full_message_template = """
+        Fix the following malformatted JSON array. Each entry must have at least the following fields:
+        {fields_list}
+
+        The provided malformed JSON array is:
+        ```json
+        {criteria_response}
+        ```
+
+        Return only the fixed JSON array and nothing else.
+        """
+        full_message = full_message_template.format(
+            fields_list=fields_list, criteria_response=criteria_response_json_array
+        )
+
+        chat_response = self.mistral_client.chat.complete(
+            model="mistral-large-latest",
+            messages=[{"role": "user", "content": full_message}],
+            response_format={"type": "json_object"},
+            temperature=MODEL_TEMPERATURE,
+        )
+
+        response_content = chat_response.choices[0].message.content
+        response_content_dict = json.loads(response_content)
+        return response_content_dict
+
+    def _validate_and_fix_llm_response(
+        self, response_content: str, required_fields: set
+    ) -> List[Dict]:
+        """
+        Validate and fix JSON response from LLM if needed.
+
+        Args:
+            response_content: String containing the LLM response that should be JSON
+            required_fields: Set of fields that must be present in each object
+
+        Returns:
+            List[Dict]: Validated JSON array matching required schema
+
+        Raises:
+            ValueError: If response cannot be parsed or fixed to match required schema
+        """
+        try:
+            result_list = json.loads(response_content)
+            if not self._validate_json_schema(result_list, required_fields):
+                print(result_list)
+                logger.warning("Response JSON does not match required schema, attempting to fix...")
+                result_list = self._fix_malformatted_json(response_content, required_fields)
+                if not self._validate_json_schema(result_list, required_fields):
+                    logger.error("Failed to fix JSON schema after attempt")
+                    raise ValueError("Could not generate valid JSON response")
+        except json.JSONDecodeError:
+            logger.warning("Invalid JSON response, attempting to fix...")
+            result_list = self._fix_malformatted_json(response_content, required_fields)
+            if not self._validate_json_schema(result_list, required_fields):
+                logger.error("Failed to fix JSON schema after attempt")
+                raise ValueError("Could not generate valid JSON response")
+
+        return result_list
+
+    def create_subtopic_summaries(self, overwrite: Optional[bool] = None):
+        """
+        Create summaries for each subtopic from an analysis JSON file.
+
+        Args:
+            overwrite (Optional[bool]): Whether to overwrite existing output file
+
+        Returns:
+            List[Dict]: List of summaries for each subtopic
+        """
+        # Use instance values if not specified
+        overwrite = self.overwrite if overwrite is None else overwrite
+
+        # Get standard file paths
+        analysis_json_path = self.path_manager.get_analysis_file_path()
+        output_path = self.path_manager.get_subtopic_summaries_path()
+
+        # Check if file exists and should be skipped
+        if self._should_skip_existing(output_path):
+            return
+
+        # Load analysis data directly to DataFrame
+        df = pd.read_json(analysis_json_path)
+
+        # Quick fix: Replace topic name (TODO: Fix naming inconsistency in upstream data)
+        df["topic"] = df["topic"].replace("Climate science", "Carbon Accounting & Integrity")
+
+        # Select the appropriate prompt file based on project_type
+        subtopic_prompt_path = self._get_prompt_path(
+            SUBTOPIC_RESUME_CURRENT_PROMPT_PATH, SUBTOPIC_RESUME_FUTURE_PROMPT_PATH
+        )
+
+        # Load the subtopic resume prompt
+        with open(subtopic_prompt_path, "r") as f:
+            print(f"Using subtopic prompt: {subtopic_prompt_path}")
+            subtopic_resume_system_prompt = f.read()
+
+        with open(SUBTOPIC_REFINE_PROMPT_PATH, "r") as f:
+            refine_summary_system_prompt = f.read()
+
+        # Template for the full message
+        subtopic_summary_prompt_template = """
+        {context_content}
+
+        -------
+        Carefully take into account the above instructions, analyze the following subtopic:
+
+        ```json
+        {input_data}
+        ```
+
+        Once you come up with the first version of the result, do a thorough step-by-step critical review based on the instructions above, try your best improve the result then return only the best version.
+        """
+
+        refine_summary_prompt_template = """
+        {refine_summary_prompt}
+
+        -------
+        Carefully apply the instructions to the following input:
+
+        ```json
+        {original_json_summary}
+        ```
+        """
+
+        summaries = []
+        # Group by topic and sub_topic
+        grouped = df.groupby(["topic", "sub_topic"])
+
+        total_groups = len(grouped)
+        for idx, ((topic, subtopic), group) in enumerate(grouped, 1):
+            logger.info(f"Processing group {idx}/{total_groups}: {topic} - {subtopic}")
+            # Convert group data to dict records
+            group_data = group.to_dict("records")
+
+            subtopic_summary_message = subtopic_summary_prompt_template.format(
+                context_content=subtopic_resume_system_prompt,
+                input_data=json.dumps(group_data, indent=2),
+            )
+
+            # Get response from Mistral
+            chat_response_1 = self.mistral_client.chat.complete(
+                model=self.model,
+                messages=[{"role": "user", "content": subtopic_summary_message}],
+                response_format={"type": "json_object"},
+                temperature=MODEL_TEMPERATURE,
+            )
+
+            summary_json = self._validate_and_fix_llm_response(
+                chat_response_1.choices[0].message.content, REQUIRED_FIELDS_FOR_SUBTOPIC_SUMMARIES
+            )
+
+            refine_summary_message = refine_summary_prompt_template.format(
+                refine_summary_prompt=refine_summary_system_prompt,
+                original_json_summary=json.dumps(summary_json, indent=2),
+            )
+
+            chat_response_2 = self.mistral_client.chat.complete(
+                model=self.model,
+                messages=[{"role": "user", "content": refine_summary_message}],
+                response_format={"type": "json_object"},
+                temperature=MODEL_TEMPERATURE,
+            )
+
+            refined_summary_json = self._validate_and_fix_llm_response(
+                chat_response_2.choices[0].message.content, REQUIRED_FIELDS_FOR_SUBTOPIC_SUMMARIES
+            )
+
+            summaries.append(refined_summary_json)
+
+            time.sleep(1)  # Rate limiting
+
+        # Save summaries using the helper method
+        self._save_json_output(summaries, output_path)
+        logger.info(f"Created summaries for {len(summaries)} subtopics.")
+
+        return summaries
+
+    def create_topic_summaries(self, overwrite: Optional[bool] = None):
+        """
+        Create summaries for each topic from subtopic summaries.
+
+        Args:
+            overwrite (Optional[bool]): Whether to overwrite existing output file
+
+        Returns:
+            List[Dict]: List of summaries for each topic
+        """
+        # Use instance values if not specified
+        overwrite = self.overwrite if not overwrite else overwrite
+
+        # Get standard file paths
+        subtopic_summaries_json_path = self.path_manager.get_subtopic_summaries_path()
+        output_path = self.path_manager.get_topic_summaries_path()
+
+        # Check if file exists and should be skipped
+        if self._should_skip_existing(output_path):
+            return
+
+        # Load analysis data directly to DataFrame
+        df = pd.read_json(subtopic_summaries_json_path)
+
+        # Quick fix: Replace topic name (TODO: Fix naming inconsistency in upstream data)
+        df["topic"] = df["topic"].replace("Climate science", "Carbon Accounting & Integrity")
+
+        # Select the appropriate prompt file based on project_type
+        topic_prompt_path = self._get_prompt_path(
+            TOPIC_RESUME_CURRENT_PROMPT_PATH, TOPIC_RESUME_FUTURE_PROMPT_PATH
+        )
+
+        # Load the topic resume prompt
+        with open(topic_prompt_path, "r") as f:
+            print(f"Using topic prompt: {topic_prompt_path}")
+            topic_resume_system_prompt = f.read()
+
+        # Template for the full message
+        topic_summary_prompt_template = """
+        {context_content}
+
+        -------
+        Carefully taking into account the above instructions, please produce the aggregated overview paragraph for the provided topic analysis:
+
+        ```json
+        {input_data}
+        ```
+        """
+
+        summaries = []
+        # Group by topic
+        grouped = df.groupby("topic")
+
+        total_groups = len(grouped)
+        for idx, (topic, group) in enumerate(grouped, 1):
+            logger.info(f"Processing topic {idx}/{total_groups}: {topic}")
+            # Convert group data to dict records
+            group_data = group.to_dict("records")
+
+            topic_summary_message = topic_summary_prompt_template.format(
+                context_content=topic_resume_system_prompt,
+                input_data=json.dumps(group_data, indent=2),
+            )
+
+            # Get response from Mistral
+            chat_response = self.mistral_client.chat.complete(
+                model=self.model,
+                messages=[{"role": "user", "content": topic_summary_message}],
+                response_format={"type": "json_object"},
+                temperature=MODEL_TEMPERATURE,
+            )
+
+            summary_json = self._validate_and_fix_llm_response(
+                chat_response.choices[0].message.content, REQUIRED_FIELDS_FOR_TOPIC_SUMMARIES
+            )
+            summaries.append(summary_json)
+
+            time.sleep(1)  # Rate limiting
+
+        # Save summaries using the helper method
+        self._save_json_output(summaries, output_path)
+        logger.info(f"Created summaries for {len(summaries)} topics.")
+
+        return summaries
+
+    def create_topic_main_insights(self, overwrite: Optional[bool] = None):
+        """
+        Generate main insights for the project based on topic summaries.
+
+        Args:
+            overwrite (Optional[bool]): Whether to overwrite existing output file
+
+        Returns:
+            Dict: Project main idea
+        """
+        # Use instance values if not specified
+        overwrite = self.overwrite if not overwrite else overwrite
+
+        # Get standard file paths
+        topic_summaries_json_path = self.path_manager.get_topic_summaries_path()
+        output_path = self.path_manager.get_main_insights_path()
+
+        # Check if file exists and should be skipped
+        if self._should_skip_existing(output_path):
+            return load_json_file(output_path)
+
+        # Load topic summaries data
+        topic_summaries = load_json_file(topic_summaries_json_path)
+
+        # Load the project main idea prompt
+        with open(PROJECT_MAIN_IDEA_PROMPT_PATH, "r") as f:
+            project_main_insight_prompt = f.read()
+
+        # Template for the full message
+        main_insight_prompt_template = """
+        {context_content}
+
+        -------
+        Carefully taking into account the above instructions, please produce the project main insights based on these topic summaries:
+
+        ```json
+        {input_data}
+        ```
+        """
+
+        main_insight_message = main_insight_prompt_template.format(
+            context_content=project_main_insight_prompt,
+            input_data=json.dumps(topic_summaries, indent=2),
+        )
+
+        # Get response from Mistral
+        chat_response = self.mistral_client.chat.complete(
+            model=self.model,
+            messages=[{"role": "user", "content": main_insight_message}],
+            response_format={"type": "json_object"},
+            temperature=MODEL_TEMPERATURE,
+        )
+
+        main_insight_json = self._validate_and_fix_llm_response(
+            chat_response.choices[0].message.content, REQUIRED_FIELDS_FOR_MAIN_INSIGHTS
+        )
+
+        # Save main idea using the helper method
+        self._save_json_output(main_insight_json, output_path)
+        logger.info(f"Created project main idea.")
+
+        return main_insight_json
+
+    def update_project_metadata(self) -> Dict:
+        """
+        Creates or updates the project metadata file with timestamp and model information.
+
+        Returns:
+            Dict: Updated metadata
+        """
+        # Get path for metadata file in the analysis directory
+        metadata_path = os.path.join(self.path_manager.get_analysis_dir(), "metadata.json")
+
+        # Initialize metadata with current information
+        metadata = {
+            "project_name": self.path_manager.project_name,
+            "model_version": self.model,
+            "last_update": time.strftime("%Y-%m-%d %H:%M:%S"),
+        }
+
+        # If metadata file exists, load it and update only necessary fields
+        if os.path.exists(metadata_path):
+            try:
+                with open(metadata_path, "r", encoding="utf-8") as f:
+                    existing_metadata = json.load(f)
+                    # Preserve existing fields, update only our specified fields
+                    metadata = {**existing_metadata, **metadata}
+            except (json.JSONDecodeError, FileNotFoundError) as e:
+                logger.warning(
+                    f"Error loading existing metadata file: {str(e)}. Creating new metadata."
+                )
+
+        # Save the updated metadata
+        self._save_json_output(metadata, metadata_path)
+        logger.info("Updated project metadata")
+
+        return metadata
+
+    def set_project_type(self, project_type: Literal["current", "future"]) -> None:
+        """
+        Set the project type for analysis.
+
+        Args:
+            project_type (str): Type of project - "current" (ongoing) or "future" (planned)
+        """
+        if project_type not in ["current", "future"]:
+            raise ValueError("Project type must be 'current' or 'future'")
+
+        self.project_type = project_type
+        logger.info(f"Project type set to: {self.project_type}")
+
+    def process_project(self) -> None:
+        """
+        Process a complete project through the entire pipeline.
+
+        Args:
+            project_folder (str): Path to the project folder
+        """
+        total_start_time = time.time()
+        logger.info(f"Starting analysis pipeline for project: {self.path_manager.project_name}")
+
+        # Step 1: Extract project overview data
+        logger.info("Step 1/5: Extracting project overview data")
+        step_start_time = time.time()
+        overview_data = self.extract_project_overview_data()
+        logger.info(
+            f"✓ Project overview extraction completed in {(time.time() - step_start_time) / 60:.2f} minutes"
+        )
+
+        # Get project_type from overview_data, default to "future" if not present or null
+        if "project_type" in overview_data and overview_data["project_type"] != "":
+            self.set_project_type(overview_data["project_type"])
+        else:
+            logger.warning(
+                "No project_type found in overview data or value is null, defaulting to 'future'"
+            )
+            self.set_project_type("future")
+
+        # Step 2: Analyze due diligence criteria
+        logger.info("Step 2/5: Analyzing due diligence criteria")
+        step_start_time = time.time()
+        self.analyze_due_diligence_criteria()
+        logger.info(
+            f"✓ Due diligence analysis completed in {(time.time() - step_start_time) / 60:.2f} minutes"
+        )
+
+        # Step 3: Create subtopic summaries
+        logger.info("Step 3/5: Creating subtopic summaries")
+        step_start_time = time.time()
+        self.create_subtopic_summaries()
+        logger.info(
+            f"✓ Subtopic summaries created in {(time.time() - step_start_time) / 60:.2f} minutes"
+        )
+
+        # Step 4: Create topic summaries
+        logger.info("Step 4/5: Creating topic summaries")
+        step_start_time = time.time()
+        self.create_topic_summaries()
+        logger.info(
+            f"✓ Topic summaries created in {(time.time() - step_start_time) / 60:.2f} minutes"
+        )
+
+        logger.info("Step 5/5: Generating main project insights")
+        step_start_time = time.time()
+        self.create_topic_main_insights()
+        logger.info(
+            f"✓ Main insights generated in {(time.time() - step_start_time) / 60:.2f} minutes"
+        )
+
+        # Step 6: Update project metadata
+        logger.info("Updating project metadata")
+        self.update_project_metadata()
+
+        total_time = time.time() - total_start_time
+        minutes, seconds = divmod(total_time, 60)
+        logger.info(
+            f"Project analysis pipeline completed in {int(minutes)} minutes {seconds:.2f} seconds"
+        )
diff --git a/sequestrae_engine/document_parsing/parser.py b/sequestrae_engine/document_parsing/parser.py
new file mode 100644
index 0000000..29d79cc
--- /dev/null
+++ b/sequestrae_engine/document_parsing/parser.py
@@ -0,0 +1,157 @@
+import logging
+import os
+import time
+
+from google import genai
+from google.genai import types
+
+from sequestrae_engine.document_parsing.extractors import PathManager
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+class PDFToMarkdownParser:
+    def __init__(self, gemini_api_key, project_folder, model="gemini-2.0-flash-001"):
+        # self.llama_api_key = llama_api_key
+        self.model = model
+        self.gemini_client = genai.Client(api_key=gemini_api_key)
+        self.path_manager = PathManager(model=model, project_folder=project_folder)
+        # No need to store project_name, PathManager already has it
+
+    """
+    def parse_pdf(self, pdf_path, output_folder_path=None, overwrite=False):
+        pdf_dir = os.path.dirname(pdf_path)
+        pdf_name = os.path.splitext(os.path.basename(pdf_path))[0]
+
+        # Determine output path
+        if output_folder_path:
+            output_path = os.path.join(output_folder_path, f"{pdf_name}.md")
+        else:
+            # Default to same directory as PDF
+            output_path = os.path.join(pdf_dir, f"{pdf_name}.md")
+
+        # Check if file already exists and respect overwrite flag
+        if os.path.exists(output_path) and not overwrite:
+            logger.info(f"Markdown file {output_path} already exists and overwrite=False")
+            return
+
+        parser = LlamaParse(
+            language="en",
+            result_type="markdown",
+            disable_image_extraction=True,
+            skip_diagonal_text=True,
+            output_tables_as_HTML=True,
+            api_key=self.llama_api_key,
+        )
+
+        file_extractor = {".pdf": parser}
+
+        logger.info(f"Parsing {pdf_path} and save to {output_path}")
+        nest_asyncio.apply()  # Required for running asyncio in Jupyter Notebook
+        documents = SimpleDirectoryReader(
+            input_files=[pdf_path], file_extractor=file_extractor
+        ).load_data()
+
+        doc_dict = documents[0].dict()
+        markdown_content = doc_dict.get("text")
+
+        self.save_markdown(markdown_content, output_path)
+    """
+
+    def save_markdown(self, markdown_content, output_path):
+        # Ensure the directory exists before writing the file
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        with open(output_path, "w") as md_file:
+            md_file.write(markdown_content)
+
+    def parse_pdf_folder(self, overwrite=False):
+        """
+        Parse PDFs in the project folder or use existing markdown files if available.
+        For each PDF, checks if a corresponding .md file exists:
+        - If yes, use the markdown content directly
+        - If no, parse the PDF to markdown
+
+        Args:
+            overwrite (bool): Whether to overwrite existing output file
+        """
+        # Get the project folder path
+        project_folder = self.path_manager.project_folder
+
+        # Get the output path for the combined markdown
+        output_path = self.path_manager.get_concatenated_doc_path()
+
+        if os.path.exists(output_path) and not overwrite:
+            logger.info(
+                f"Output file {output_path} already exists and overwrite=False. Skipping processing."
+            )
+            return
+
+        # Get all PDF files in the folder
+        pdf_files = [f for f in os.listdir(project_folder) if f.lower().endswith(".pdf")]
+
+        if not pdf_files:
+            logger.warning(f"No PDF files found in {project_folder}")
+            return
+
+        combined_content = []
+
+        # Process each PDF file
+        for pdf_file in pdf_files:
+            base_name = os.path.splitext(pdf_file)[0]
+            md_file = f"{base_name}.md"
+            md_path = os.path.join(project_folder, md_file)
+
+            # Check if corresponding markdown file exists
+            if os.path.exists(md_path):
+                logger.info(f"Using existing markdown file for: {pdf_file}")
+                with open(md_path, "r") as f:
+                    doc_content = f.read()
+            else:
+                # Parse the PDF if no markdown exists
+                pdf_path = os.path.join(project_folder, pdf_file)
+                logger.info(f"Processing PDF: {pdf_file}")
+
+                with open(pdf_path, "rb") as f:
+                    pdf_bytes = f.read()
+
+                prompt = """
+                Perform OCR on the following pages and convert the content into Markdown format.
+
+                - Output **only** the Markdown text—do not include any additional comments, explanations, or summaries.
+                - The document pertains to a biochar project and may include project documentation, audit reports, or related materials.
+                - Ignore the table of contents—it is not needed.
+                - Preserve section headings, bullet points, and emphasized text (bold, italic) as they appear in the document.
+
+                ### Table Handling:
+                - **Extract all tables** from the document—**do not skip any tables.**
+                - Convert tables into **structured and readable Markdown tables** instead of HTML.
+                - If a table's format is unclear or difficult to read, **restructure or transpose it** for better clarity while preserving the original meaning.
+                - If a table is spread across multiple pages, **reconstruct it properly** in the output.
+                - If the document contains tabular data without clear table formatting, **detect and format it as a Markdown table** to improve readability.
+                """
+                response = self.gemini_client.models.generate_content(
+                    model=self.model,
+                    contents=[
+                        types.Part.from_bytes(
+                            data=pdf_bytes,
+                            mime_type="application/pdf",
+                        ),
+                        prompt,
+                    ],
+                )
+
+                doc_content = response.text
+                time.sleep(1)  # Sleep for 1 second to avoid rate limiting
+
+            file_section = (
+                f"# The following text is the content of file: {pdf_file}\n{doc_content}\n\n"
+            )
+            combined_content.append(file_section)
+
+        # Save combined content
+        final_content = "".join(combined_content)
+        self.save_markdown(final_content, output_path)
+        logger.info(f"Combined markdown saved to {output_path}")
diff --git a/sequestrae_engine/document_parsing/prompts/due_diligence_criteria.csv b/sequestrae_engine/document_parsing/prompts/due_diligence_criteria.csv
new file mode 100644
index 0000000..e98445b
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/due_diligence_criteria.csv
@@ -0,0 +1,184 @@
+topic,sub_topic,risk_factor,question,instruction
+Carbon Accounting & Integrity,Permanence,H/Corg Ratio Compliance,"Does the project measure the H/Corg ratio using recognized protocols (e.g., CHNS/O analysis, EBC or IBI guidelines)?","Look for: Measurement logs in project documentation, references to recognized lab standards (EBC, IBI), and mention of equipment or lab certifications | If absent/vague: Check if only partial references are included (e.g., 'we tested biochar' without details) or if the project only claims compliance verbally | Assess: If protocols are not specified or standards are absent, data reliability is low and the project may risk failing recognized stability thresholds."
+Carbon Accounting & Integrity,Permanence,H/Corg Ratio Compliance,Are reported H/Corg values consistently below 0.7 across multiple production batches?,"Look for: Multiple test results in audit reports showing H/Corg values < 0.7 with clear batch sampling information | If absent/vague: Check if only one test is provided or if batch variability is not mentioned | Assess: Inconsistent or high H/Corg values indicate potential instability, reducing confidence in long-term permanence."
+Carbon Accounting & Integrity,Permanence,H/Corg Ratio Compliance,Does the project use accredited third-party laboratories to validate the H/Corg ratio?,"Look for: Explicit documentation of lab accreditation (e.g., ISO 17025) and third-party verification in certification reports | If absent/vague: Check if the project relies solely on internal testing with no external validation | Assess: Third-party validation increases credibility, whereas self-reported data raises uncertainty."
+Carbon Accounting & Integrity,Permanence,H/Corg Ratio Compliance,How does the project address any out-of-range H/Corg values or incomplete ratio measurements?,"Look for: Evidence of corrective actions such as process adjustments, re-testing protocols, or documented plans for discarding off-spec batches | If absent/vague: Check if there is only an ideal ratio mentioned without discussing deviations | Assess: A lack of corrective action indicates risk in maintaining consistent biochar quality."
+Carbon Accounting & Integrity,Permanence,Pyrolysis Temperature Compliance,What pyrolysis temperature range (≥550°C recommended) is targeted to ensure stable biochar with low volatile content?,"Look for: Project documentation specifying operating temperatures (ideally 550–700°C), with references to best-practice guidelines | If absent/vague: Check if the project only generically mentions 'high temperature' without numeric details | Assess: Operating below ~550°C can yield unstable biochar, so clear temperature data is essential."
+Carbon Accounting & Integrity,Permanence,Pyrolysis Temperature Compliance,Does the project provide temperature data logs or real-time monitoring across different production batches?,"Look for: Automated system outputs (e.g., thermocouple logs) or documented temperature records with batch IDs | If absent/vague: Check if only a single snapshot or generic statement is provided | Assess: Consistent temperature records across batches are critical for quality control; sporadic data increases risk."
+Carbon Accounting & Integrity,Permanence,Pyrolysis Temperature Compliance,"Which pyrolysis technology is employed, and does it reliably achieve high-temperature operation?","Look for: Detailed technical descriptions naming the reactor type (e.g., slow pyrolysis, gasification) and any manufacturer references | If absent/vague: Check if the technology is only mentioned in passing without performance data | Assess: Established high-temperature systems provide more stable biochar; vague descriptions increase uncertainty."
+Carbon Accounting & Integrity,Permanence,Pyrolysis Temperature Compliance,How does the project handle process control if temperatures drop below the target range?,"Look for: Standard operating procedures (SOPs), alarm triggers, or documented adjustments in audit reports | If absent/vague: Check if the project only states that it maintains high temperatures without details | Assess: Lack of process control increases the risk of producing low-quality, unstable biochar."
+Carbon Accounting & Integrity,Permanence,Testing & Verification of Stability,"Does the project test additional stability parameters (volatile matter, fixed carbon) beyond the H/Corg ratio?","Look for: Lab results in audit reports detailing proximate/ultimate analyses, including volatile matter and fixed carbon percentages | If absent/vague: Check if only H/Corg values are provided | Assess: Multiple stability metrics enhance confidence in the biochar’s permanence."
+Carbon Accounting & Integrity,Permanence,Testing & Verification of Stability,Are multiple samples from different production batches tested for consistency in stability indicators?,Look for: Sampling protocols with results per batch in certification reports or technical annexes | If absent/vague: Check if only one-time test results are given without batch data | Assess: Consistency across batches indicates robust quality control; isolated tests suggest higher risk.
+Carbon Accounting & Integrity,Permanence,Testing & Verification of Stability,"Does the project follow established certification standards (e.g., EBC, IBI, Puro) for stability testing?",Look for: Explicit references to certification bodies or adherence to their guidelines in project documents | If absent/vague: Check if the project self-certifies without citing any standard | Assess: Recognized third-party certification adds reliability; absence raises credibility issues.
+Carbon Accounting & Integrity,Permanence,Testing & Verification of Stability,"How frequently are stability tests performed, and is there a plan for long-term re-testing?","Look for: Statements in the MRV plan specifying testing intervals (e.g., monthly, quarterly) and future schedules | If absent/vague: Check if tests are mentioned only at project start | Assess: Regular testing is key to tracking potential changes; infrequent tests introduce uncertainty."
+Carbon Accounting & Integrity,Permanence,Feedstock Consistency & Impact on Stability,"Which specific feedstocks are used, and do they remain consistent to ensure uniform biochar quality?","Look for: Detailed feedstock lists and sourcing agreements, including supplier names/types | If absent/vague: Check if only vague terms like 'local biomass' are used | Assess: Consistency supports stable biochar; variability can lead to quality fluctuations."
+Carbon Accounting & Integrity,Permanence,Feedstock Consistency & Impact on Stability,"Are contaminants or impurities (heavy metals, plastics) screened out in the feedstock to prevent compromised biochar quality?","Look for: Documented screening protocols, supplier certifications, or lab analyses on raw materials | If absent/vague: Check if the project only makes a generic claim of 'clean feedstock' | Assess: Unscrutinized feedstock may introduce contaminants, affecting stability and safety."
+Carbon Accounting & Integrity,Permanence,Feedstock Consistency & Impact on Stability,Does the project adjust pyrolysis parameters if feedstock composition changes?,"Look for: Process control documentation describing adjustments (temperature, residence time) for different feedstock properties | If absent/vague: Check if a single process is used for all feedstock types | Assess: Adaptive control ensures consistent biochar quality despite variability."
+Carbon Accounting & Integrity,Permanence,Storage & Application Risks,How is biochar stored prior to application to prevent oxidation or moisture-related degradation?,"Look for: Storage descriptions (sealed containers, covered warehouses, dry conditions) in project or audit reports | If absent/vague: Check if only generic mentions of 'proper storage' exist | Assess: Poor storage conditions risk premature degradation and reduce permanence."
+Carbon Accounting & Integrity,Permanence,Storage & Application Risks,"Does the project verify biochar stability after application (e.g., via soil sampling or field monitoring)?","Look for: Post-application monitoring protocols, including soil sample analyses or performance studies | If absent/vague: Check if no follow-up verification is mentioned | Assess: Without post-application checks, long-term permanence claims are not substantiated."
+Carbon Accounting & Integrity,Permanence,Storage & Application Risks,"Is there a risk that biochar will be repurposed or disturbed (e.g., used as fuel), leading to carbon re-release?","Look for: End-use contracts that specify final use, chain-of-custody documentation, or legal prohibitions on repurposing | If absent/vague: Check if usage terms are unclear or not enforced | Assess: Potential re-use as fuel undermines permanence, increasing reversal risk."
+Carbon Accounting & Integrity,Permanence,Storage & Application Risks,"Does the project monitor applied biochar to ensure it remains undisturbed (e.g., minimal tillage or erosion)?","Look for: Post-application monitoring protocols (soil integrity checks, remote sensing, field inspections) | If absent/vague: Check if no follow-up is described | Assess: Physical disturbances (tillage, erosion) can release carbon, reducing overall effectiveness."
+Carbon Accounting & Integrity,Permanence,Longevity of Biochar in Intended Use,"Has the project estimated biochar’s expected lifespan (e.g., 100–1000+ years) using modeling or field trial data?","Look for: Peer-reviewed decay models, long-term field trial data, or climate/soil condition comparisons | If absent/vague: Check if only generic statements of 'permanence' are made | Assess: Robust lifespan estimates from empirical data enhance credibility; vague claims do not."
+Carbon Accounting & Integrity,Permanence,Longevity of Biochar in Intended Use,"Does the project account for local environmental factors (temperature, moisture, microbial activity) that might accelerate biochar degradation?",Look for: LCA or technical report discussions with climate/soil-specific adjustments in longevity modeling | If absent/vague: Check if only a global average or generic factor is used | Assess: Ignoring local conditions can lead to overestimated permanence.
+Carbon Accounting & Integrity,Permanence,Longevity of Biochar in Intended Use,Are legal or operational measures in place to prevent future land-use changes that could disturb the biochar?,"Look for: Land management agreements, easements, or restrictions in project or audit reports | If absent/vague: Check if land use is only broadly described | Assess: Without protections, future land-use changes can negate long-term carbon sequestration."
+Carbon Accounting & Integrity,Permanence,Anthropogenic Disturbance Risks,"Beyond normal storage/application, could external human activities (e.g., unplanned land conversion, industrial contamination) compromise biochar’s stored carbon?",Look for: Project documentation on broader threats like infrastructure expansion or contamination sources outside project control | If absent/vague: The project may only mention typical farm operations | Assess: High exposure to outside disturbances increases the risk of reversals.
+Carbon Accounting & Integrity,Permanence,Natural Disturbance Risks,"Does the project consider climatic or soil factors (e.g., extreme weather, flooding, erosion) that might accelerate biochar loss or redistribution?","Look for: Risk assessments for extreme weather events or harsh soil conditions, plus any mitigation strategies | If absent/vague: Check if the project only cites 'normal conditions' | Assess: Without planning for natural disturbances, permanence is less certain."
+Carbon Accounting & Integrity,Permanence,Carbon Rights & Legal Security,"Are the legal rights to the stored carbon clearly established, and do land or resource tenure uncertainties pose a risk to long-term storage?","Look for: Land deeds or contracts granting the project or landowner the right to claim carbon credits over the credited period | If absent/vague: Check for potential disputes or government claims | Assess: Unclear carbon rights can lead to disputes or discontinuation, threatening permanence."
+Carbon Accounting & Integrity,Permanence,Non-Permanence Risk Mitigation,"Has the project implemented mechanisms (e.g., buffer pools, insurance) to address potential carbon reversals?","Look for: Evidence that a portion of credits is set aside, formal insurance coverage, or contractual remedies for reversals | If absent/vague: Check if the project simply asserts 'long-term permanence' with no fallback | Assess: Lack of formal reversal safeguards raises overall permanence risk."
+Carbon Accounting & Integrity,Crediting accuracy,Completeness & Scope of LCA,"Does the project present a full LCA covering feedstock sourcing, transport, pyrolysis, and end-use impacts?","Look for: Detailed LCA documents following ISO 14044 (or similar), with cradle-to-grave system boundaries | If absent/vague: Check if transport or end-use are omitted or treated superficially | Assess: Incomplete LCAs may overestimate net removals, reducing credibility."
+Carbon Accounting & Integrity,Crediting accuracy,Completeness & Scope of LCA,"Are assumptions regarding avoided emissions (e.g., open burning, landfill decay) clearly defined and supported by local data?","Look for: A baseline scenario detailing what happens to biomass if not converted, including local waste management references | If absent/vague: Assumptions might be generic or unsubstantiated | Assess: Overly optimistic baselines inflate credit claims."
+Carbon Accounting & Integrity,Crediting accuracy,Completeness & Scope of LCA,Has the LCA been updated with actual operational data rather than relying solely on theoretical estimates?,"Look for: Recent audit or operational data on feedstock volumes, energy use, biochar yields replacing old feasibility assumptions | If absent/vague: Only references to initial feasibility studies | Assess: Real operational data improves confidence in the carbon credit calculation."
+Carbon Accounting & Integrity,Crediting accuracy,Emission Factors & Data Transparency,"Are CO₂, CH₄, and N₂O emission factors based on site-specific measurements or credible references, with proper citations?","Look for: Citations to IPCC, Ecoinvent, or local field data in the LCA or project documents | If absent/vague: The project may rely on generic global factors with no context | Assess: Transparent, credible emission factors reduce miscalculation risk."
+Carbon Accounting & Integrity,Crediting accuracy,Emission Factors & Data Transparency,"Are non-CO₂ gases (CH₄, N₂O) correctly weighted using the latest IPCC GWP factors (e.g., AR6)?","Look for: Mention of updated GWP100 values for CH₄ and N₂O, or references to current IPCC guidelines | If absent/vague: Check if older values are used without explanation or if these gases are ignored | Assess: Proper weighting is essential for accurate net emission calculations."
+Carbon Accounting & Integrity,Crediting accuracy,Accounting for Non-CO₂ Greenhouse Gases,Does the project measure or estimate methane slip during pyrolysis and consider N₂O flux changes in biochar-amended soils?,"Look for: Gas analysis results for pyrolysis exhaust and any soil emission studies in audit/project documents | If absent/vague: The project may focus only on CO₂, ignoring CH₄ or N₂O | Assess: Omitting these gases can significantly distort net removals."
+Carbon Accounting & Integrity,Crediting accuracy,Accounting for Non-CO₂ Greenhouse Gases,Does the baseline scenario include CH₄ and N₂O emissions from uncontrolled biomass decay or disposal?,"Look for: Baseline comparisons quantifying CH₄/N₂O in open burning, decay, or landfills | If absent/vague: Check if only CO₂ is factored, or if CH₄/N₂O are set to zero without reason | Assess: Incomplete baseline accounting overestimates carbon credits."
+Carbon Accounting & Integrity,Crediting accuracy,Energy Use & Carbon Intensity of Pyrolysis,"How does the project quantify energy consumption during pyrolysis, and is this factored into net emissions?","Look for: Detailed metrics (e.g., MJ/ton biochar), fuel type breakdowns, and resulting emissions in the LCA | If absent/vague: Only qualitative statements like 'low energy use' without numbers | Assess: High energy use can erode net carbon benefits, so transparency is essential."
+Carbon Accounting & Integrity,Crediting accuracy,Energy Use & Carbon Intensity of Pyrolysis,"Does the project implement energy optimization measures (e.g., waste heat recovery, syngas reuse)?","Look for: Process flow diagrams or audit references to captured syngas or recirculated heat for operations | If absent/vague: The project might only claim 'we minimize energy' without detail | Assess: Optimized energy use reduces overall emissions, strengthening net removal claims."
+Carbon Accounting & Integrity,Crediting accuracy,Baseline Scenario Validity,"If the project claims a zero or minimal baseline, what evidence shows no prior carbon-removal or GHG-reduction activities existed on-site?","Look for: Historical records proving no comparable practice (e.g., no composting, no prior biochar) | If absent/vague: Project might simply assert 'zero baseline' with no data | Assess: Unsubstantiated zero baselines create high risk of over-crediting."
+Carbon Accounting & Integrity,Crediting accuracy,Alternative Feedstock Scenarios,"Does the project evaluate other plausible feedstock uses (e.g., composting, competing biomass industries) that might have stored or reduced emissions elsewhere?","Look for: Market/industry data on typical biomass uses in the region | If absent/vague: The project may label everything 'waste' without proof | Assess: Ignoring valuable alternative uses can inflate net removals, leading to over-crediting."
+Carbon Accounting & Integrity,Crediting accuracy,Project Emissions Completeness,"Are all project-related emissions (fossil fuel use, electricity, transport, infrastructure) accounted for and deducted from net credits?","Look for: A comprehensive breakdown of direct/indirect emissions in the LCA | If absent/vague: Some sources (e.g., transport) may be omitted | Assess: Omitting significant sources inflates net removals and risks over-crediting."
+Carbon Accounting & Integrity,Crediting accuracy,Carbon Accounting Uncertainty,"Does the project quantify uncertainty (error margins, confidence intervals) in carbon accounting, following a recognized standard?","Look for: Uncertainty analyses or confidence intervals, references to Verra/Puro/Riverse guidelines | If absent/vague: The project may note uncertainty without numeric estimates | Assess: Ignoring uncertainty risks over-crediting; recognized standards add credibility."
+Carbon Accounting & Integrity,Crediting accuracy,Safeguards Against Over-Crediting,Has the project adopted conservative assumptions or buffers to mitigate overstating net removals?,"Look for: Discount factors, buffers, or partial credit issuance to account for unknowns | If absent/vague: The project may claim maximum removals with no margin | Assess: Without conservative safeguards, over-crediting risk is higher."
+Carbon Accounting & Integrity,Additionality,Financial Additionality & Project Viability,"Would the project proceed without carbon credit revenue, or is there a clear financial gap making carbon finance essential?",Look for: Detailed financial models or cost-benefit analyses showing capital/operational shortfalls without credits | If absent/vague: Check if the project simply states credits help without data | Assess: A genuine funding gap supports additionality; profitability otherwise undermines it.
+Carbon Accounting & Integrity,Additionality,Financial Additionality & Project Viability,Have alternative subsidies or funding sources been disclosed to verify if carbon credits fill a genuine shortfall?,"Look for: Disclosure of government grants, tax incentives, or private investments in financial docs | If absent/vague: Only broad claims about partial funding | Assess: Substantial alternative funding may reduce reliance on carbon credits, weakening additionality."
+Carbon Accounting & Integrity,Additionality,Investment Analysis,"Is there a financial model (IRR, ROI, payback period) showing how carbon credit revenue affects project profitability?","Look for: Explicit references to IRR, ROI, NPV, or payback period and statements on carbon credits’ role | If absent/vague: Seek indirect clues about bridging cost gaps | Assess: Clear statements of credit dependence strengthen additionality."
+Carbon Accounting & Integrity,Additionality,Investment Analysis,"Which key assumptions (CAPEX, OPEX, discount rate, feedstock costs, carbon price) underpin the project’s financial model, and how are they substantiated?","Look for: Specific numeric assumptions tied to quotations, benchmarks, or third-party data, with auditor commentary | If absent/vague: Only qualitative references to major cost drivers | Assess: Unchecked or unrealistic assumptions weaken financial additionality claims."
+Carbon Accounting & Integrity,Additionality,Investment Analysis,"Does the project include scenario or sensitivity analyses (±10–20% in CAPEX, carbon price) to show IRR/profitability changes?",Look for: Tables or separate sections illustrating how financial returns shift with key variable changes | If absent/vague: Claims like 'a carbon price below $X is infeasible' with no data | Assess: Thorough sensitivity analysis clarifies the project’s robustness.
+Carbon Accounting & Integrity,Additionality,Investment Analysis,Are the benchmarks or hurdle rates used for additionality appropriate for this technology and region?,Look for: References to typical sector IRRs or cost of capital relevant to local conditions | If absent/vague: Generic statements like 'industry standard' with no data | Assess: Artificially high/low hurdles can distort additionality findings.
+Carbon Accounting & Integrity,Additionality,Investment Analysis,"Does the project quantify and justify high capital expenditures, and do those costs align with typical ranges for similar biochar technologies?","Look for: CAPEX breakdown referencing technology, infrastructure, permitting; compare with known industry data | If absent/vague: Claims of 'substantial upfront cost' but no detail | Assess: Credible high CAPEX can justify carbon finance; vague numbers do not."
+Carbon Accounting & Integrity,Additionality,Investment Analysis,"How do other financing streams (grants, subsidies) fit into the model, and do they undermine or reinforce the need for carbon credits?","Look for: Auditor statements on the proportion of total costs covered by external funding | If absent/vague: Indirect references to 'multiple funding partners' | Assess: Large alternative funding may reduce reliance on credits, weakening additionality (unless CAPEX is still unmet)."
+Carbon Accounting & Integrity,Additionality,Investment Analysis,Are there data gaps or auditor disclaimers limiting confidence in the project’s financial analysis?,"Look for: Auditor notes on insufficient data, unvalidated cost estimates, or high uncertainty in feedstock supply | If absent/vague: The project may not acknowledge data shortcomings | Assess: Large uncertainties reduce the credibility of additionality claims."
+Carbon Accounting & Integrity,Additionality,Revenue Analysis,"What are the primary and secondary revenue streams (e.g., biochar sales, energy, carbon credits), and what share does carbon finance represent?","Look for: Explicit breakdown (e.g., 70% from product sales, 30% from credits) or auditor confirmations | If absent/vague: Generic claims about 'major carbon revenue' with no data | Assess: If credit revenue is small, project may be viable without it."
+Carbon Accounting & Integrity,Additionality,Revenue Analysis,Do carbon credit revenue estimates align with known market benchmarks and local product prices?,"Look for: Stated carbon credit price assumptions, volumes sold, comparisons to typical rates | If absent/vague: Vague references like 'premium prices' with no data | Assess: Overstated credit revenue can falsely inflate additionality arguments."
+Carbon Accounting & Integrity,Additionality,Revenue Analysis,"Is carbon finance portrayed as critical for viability, or more like an incremental bonus?","Look for: Statements such as 'Project depends on carbon credits' vs. 'Credits are an added benefit' | If absent/vague: Indirect mentions of bridging operational gaps | Assess: If it’s essential, that supports additionality; if purely incremental, less so."
+Carbon Accounting & Integrity,Additionality,Revenue Analysis,"Does the project account for other potential revenue sources (philanthropic grants, equity financing, government incentives), and do they reduce reliance on carbon credits?","Look for: Mention of philanthropic funds or major investors, plus auditor views on whether these sources cover most costs | If absent/vague: Only high-level references to 'government support' | Assess: If alternative funds are large, carbon finance might be secondary."
+Carbon Accounting & Integrity,Additionality,Revenue Analysis,Are any claims that carbon revenue is the 'majority' of total revenue verified by data?,"Look for: Auditor confirmations or data tables showing revenue splits | If absent/vague: Project statements may contradict actual revenue breakdowns | Assess: If no evidence is provided, the claim may be overstated."
+Carbon Accounting & Integrity,Additionality,Revenue Analysis,"Does carbon finance directly influence decisions on project design, scale, or timelines?",Look for: References to expansion or equipment upgrades specifically tied to carbon revenue | If absent/vague: Only broad statements like 'carbon market signals' | Assess: A direct link to scale/design strongly supports additionality.
+Carbon Accounting & Integrity,Additionality,Revenue Analysis,Are there disclaimers about future carbon price volatility or other uncertainties that might change the project’s dependence on carbon finance?,"Look for: Risk statements on fluctuating carbon markets, feedstock supply, or product demand | If absent/vague: No mention of potential price drops or demand shifts | Assess: Large volatility can undermine claims that credits remain essential long term."
+Carbon Accounting & Integrity,Additionality,Benefit-Sharing,"Is there a formal mechanism to share benefits (e.g., revenue allocation, community fund) with local stakeholders, and is it explicitly tied to carbon finance?",Look for: Agreements stating a portion of carbon revenue supports community benefits | If absent/vague: Only vague references to 'community benefit' with no details | Assess: Tying local benefits to credit sales can reinforce additionality via stakeholder support.
+Carbon Accounting & Integrity,Additionality,Benefit-Sharing,"Are details (eligibility, distribution criteria, timeline) of the benefit-sharing arrangement clearly defined and communicated to communities?","Look for: Documentation explaining how benefits are allocated, stakeholder consultations, or auditor confirmations | If absent/vague: Only general statements about 'plans' with no specificity | Assess: Transparent, structured benefit-sharing fosters local buy-in."
+Carbon Accounting & Integrity,Additionality,Benefit-Sharing,"Does the project show how benefit-sharing will be financed over its lifetime, and does it depend on ongoing carbon credit revenue?",Look for: Budget lines earmarking a share of credit revenue vs. up-front grants; auditor notes on funding continuity | If absent/vague: No explanation of sustaining these benefits | Assess: Reliance on continuous credit sales can strengthen additionality if no other funding is feasible.
+Carbon Accounting & Integrity,Additionality,Benefit-Sharing,Is there evidence local stakeholders were consulted (FPIC) and that grievance mechanisms exist around benefit-sharing?,"Look for: Records of community meetings, committees, or complaint-handling processes | If absent/vague: Only generic references to 'outreach' | Assess: Genuine consultation and recourse reduce conflict risk and enhance project legitimacy."
+Carbon Accounting & Integrity,Additionality,Benefit-Sharing,"Does the project link benefit-sharing to long-term sustainability (e.g., sustaining feedstock supply or local stewardship)?","Look for: Statements that ongoing local incentives ensure stable project operations or protect land use | If absent/vague: No mention of how benefits keep the community engaged | Assess: If benefits vanish, local support might wane, reducing additionality."
+Carbon Accounting & Integrity,Additionality,Regulatory & Policy Additionality,"Is the project voluntary (not legally mandated), and does it avoid overlap with regulated compliance markets?",Look for: Project docs clarifying that biochar production is not a legal requirement and that credits aren’t double-counted in cap-and-trade | If absent/vague: No clear separation of voluntary vs. compliance | Assess: Mandatory or double-counted projects are not additional.
+Carbon Accounting & Integrity,Additionality,Regulatory & Policy Additionality,Does the project benefit from policy incentives that already cover a substantial portion of costs?,"Look for: Tax breaks, feed-in tariffs, or other supports in the financial statements | If absent/vague: Only general mentions of subsidies with no specifics | Assess: Strong incentives alone may reduce credit-driven additionality."
+Carbon Accounting & Integrity,Additionality,Regulatory & Policy Additionality,"Could strong policy support (e.g., mandates, subsidies) make biochar likely even without carbon credits?","Look for: National or local regulations or large-scale government programs promoting biochar | If absent/vague: No detail on direct mandates | Assess: If policy alone could drive adoption, additionality is weaker."
+Carbon Accounting & Integrity,Additionality,Market & Demand-Driven Additionality,Does a robust market for biochar suggest the project would be profitable without carbon credits?,"Look for: Market/auditor references showing high demand or favorable pricing | If absent/vague: Only generic claims about local interest | Assess: If strong demand already exists, reliance on credits may be unnecessary."
+Carbon Accounting & Integrity,Additionality,Market & Demand-Driven Additionality,Is biochar production already common and profitable in this region?,Look for: Evidence of other commercial biochar facilities operating without carbon credits | If absent/vague: No local industry context is provided | Assess: Established practice reduces additionality unless new barriers exist.
+Carbon Accounting & Integrity,Additionality,Technological Additionality & Barriers,"Does the project use innovative pyrolysis technology not yet widely deployed, implying high upfront costs or technical uncertainty?","Look for: Novel reactor designs, advanced gas cleaning, R&D references, or pilot-phase descriptions | If absent/vague: Only says 'modern technology' with no detail | Assess: Innovations usually require carbon finance if costs/risks are high."
+Carbon Accounting & Integrity,Additionality,Technological Additionality & Barriers,"Are technical barriers (e.g., lack of trained workforce, high maintenance) documented as reasons why carbon revenue is needed?",Look for: Operational or financial reports linking credit revenue to specialized training or maintenance spending | If absent/vague: No clear mention of how these hurdles get solved | Assess: Evident technical barriers bolster additionality.
+Carbon Accounting & Integrity,Additionality,Barrier Analysis,"Does the project clearly identify barriers (technical, policy, knowledge gaps) that prevented biochar adoption before carbon finance?","Look for: Statements on how high CAPEX, lack of expertise, or policy hurdles delayed implementation | If absent/vague: Only generic references to 'unfavorable conditions' | Assess: A strong barrier analysis ties carbon credits to overcoming these hurdles."
+Carbon Accounting & Integrity,Additionality,Barrier Analysis,"Are these barriers corroborated by external references (academic studies, market data), confirming their relevance?",Look for: Citations or third-party data supporting claims of cost or technical barriers | If absent/vague: Generic statements with no proof | Assess: Well-substantiated barriers strengthen additionality claims.
+Carbon Accounting & Integrity,Additionality,Barrier Analysis,"Does carbon finance directly mitigate these barriers, or could alternative funding sources have achieved the same effect?","Look for: Documentation showing how credits were used to purchase critical equipment or fund R&D | If absent/vague: Only broad claims about 'need for extra funds' | Assess: If alternative funding was readily available, credit-driven additionality is weaker."
+Carbon Accounting & Integrity,Additionality,Common Practice Analysis,Is biochar production or similar removal activity not already widely practiced in the region without carbon finance?,"Look for: Comparisons to other local facilities, or local industry norms showing low adoption | If absent/vague: Project may assert 'no other projects' without data | Assess: If it’s indeed uncommon, additionality is stronger."
+Carbon Accounting & Integrity,Additionality,Common Practice Analysis,Do market data or studies confirm the rarity or novelty of this biochar technology in this location?,"Look for: External databases, industry reports, or academic references. | If absent/vague: Only broad claims of 'nascent market' | Assess: Without supporting references, the claim is weaker."
+Carbon Accounting & Integrity,Additionality,First-of-Its-Kind Analysis,"Does the project claim to be 'first of its kind' or a pilot in this region, and on what basis (technology novelty, scale, location)?","Look for: Direct statements about pioneering status, validated by data or auditor notes | If absent/vague: Only says 'we are the first' without references | Assess: Being first often enhances additionality if well-evidenced."
+Carbon Accounting & Integrity,Additionality,First-of-Its-Kind Analysis,"Is there verifiable evidence (market data, government sources) that no similar biochar projects existed before this start date?","Look for: Official records, NGO/govt statements, or audits confirming no prior facilities | If absent/vague: Only 'we found none' with no data | Assess: Lack of contradictory examples supports the claim."
+Carbon Accounting & Integrity,Additionality,Identification of Alternatives,"Does the project outline plausible alternative scenarios (e.g., different disposal methods, smaller-scale pyrolysis) and why they would yield fewer carbon benefits?",Look for: Baseline or scenario analysis describing business-as-usual disposal or smaller-scale operations | If absent/vague: Unclear if the project truly beats alternatives | Assess: A robust alternative analysis supports additionality.
+Carbon Accounting & Integrity,Additionality,Identification of Alternatives,Are the likelihood and evidence for each identified alternative documented with local/industry data?,Look for: Quantitative or qualitative justifications on which scenarios are realistic vs. improbable | If absent/vague: Generic statements like 'nobody would do X anyway' | Assess: Thorough comparisons strengthen the additionality argument.
+Carbon Accounting & Integrity,Additionality,Regulatory Surplus,"Does the project exceed legal requirements or mandatory practices, ensuring it isn’t just meeting existing regulations?","Look for: Evidence that biochar activity is beyond minimum standards, not mandated by local law | If absent/vague: Possibly the project only meets known legal thresholds | Assess: Meeting a legal requirement alone isn’t additional."
+Carbon Accounting & Integrity,Additionality,Legal Analysis & Ownership,Are land-use and resource rights clearly documented so the project can credibly claim carbon credits over the entire crediting period?,Look for: Signed land/tenure agreements specifying carbon ownership rights | If absent/vague: Potential disputes or short-term leases are red flags | Assess: Unclear ownership undermines additionality if the project can’t ensure stable carbon storage.
+Carbon Accounting & Integrity,Additionality,Baseline Scenario & Counterfactual Analysis,"Is the baseline scenario (e.g., open burning, landfill decay) supported by local data, not just generic assumptions?",Look for: LCA sections specifying the typical disposal method with quantitative emissions data | If absent/vague: Only broad claims about 'avoided emissions' | Assess: A credible baseline is vital for proving real removals.
+Carbon Accounting & Integrity,Additionality,Baseline Scenario & Counterfactual Analysis,Does the project account for future policy or market shifts that might change the baseline over time?,"Look for: Periodic baseline reassessments in the MRV plan, references to regulatory trends | If absent/vague: Baseline set once with no re-evaluation | Assess: A dynamic approach is more credible; static assumptions risk over-crediting as conditions evolve."
+Carbon Accounting & Integrity,Additionality,Double Counting & Incentivization Risks,Is there any risk of double counting the same carbon removal across multiple registries or programs?,"Look for: Legal agreements or auditor notes confirming exclusive registration, chain-of-custody for credits | If absent/vague: Project listed in multiple programs without clarity | Assess: Double counting invalidates credits; exclusivity is mandatory."
+Carbon Accounting & Integrity,Additionality,Double Counting & Incentivization Risks,Could substantial subsidies or co-payments create a windfall if carbon credits are stacked on top?,Look for: Full financial disclosures of all revenue streams (government or private) with breakdowns | If absent/vague: Project vaguely mentions 'some subsidies' | Assess: Over-subsidization indicates carbon credits may not fill a genuine gap.
+Carbon Accounting & Integrity,Leakage,Feedstock Displacement & Competition,"Does biomass sourcing divert materials from other carbon-storing uses (e.g., composting, timber) or create supply shortages for existing industries?",Look for: Market analysis or auditor notes on prior uses of the feedstock and local demand | If absent/vague: Project claims 'waste biomass' without verifying prior use | Assess: Diverting high-value biomass can negate net climate benefits.
+Carbon Accounting & Integrity,Leakage,Feedstock Displacement & Competition,Has the project addressed potential overharvesting or unsustainable extraction practices that might cause emissions offsite?,"Look for: Sustainability criteria, rotation schedules, or auditor comments on feedstock harvesting | If absent/vague: Project assumes unlimited supply with no supporting data | Assess: Unsustainable sourcing can cause offsite ecosystem damage and additional emissions."
+Carbon Accounting & Integrity,Leakage,Indirect Land-Use Change (ILUC),Could increased biomass demand indirectly trigger deforestation or agricultural expansion beyond the project boundary?,"Look for: Land-use impact assessments discussing local deforestation rates, potential for expansion | If absent/vague: Only generic statements on 'sustainable sourcing' | Assess: Ignoring ILUC risks leads to unaccounted emissions that offset carbon benefits."
+Carbon Accounting & Integrity,Leakage,Indirect Land-Use Change (ILUC),"Has the project enforced safeguards (e.g., sourcing only residues, using marginal lands) to avoid land-use changes?","Look for: Specific feedstock sourcing policies/maps, supplier contracts limiting deforestation | If absent/vague: Only 'we source sustainably' with no criteria | Assess: Concrete safeguards reduce ILUC risk; vague claims do not."
+Carbon Accounting & Integrity,Leakage,Market Substitution Effects,"Does the project’s biomass demand displace other low-carbon uses, causing increased fossil fuel reliance elsewhere?","Look for: Comparisons of feedstock usage vs. alternative industries (biogas, composting) in local market reports | If absent/vague: Only claims of 'abundance' with no displacement analysis | Assess: Substitution effects may undermine net climate gains."
+Carbon Accounting & Integrity,Leakage,Market Substitution Effects,Does the project monitor shifts in feedstock pricing or availability that might indicate leakage into other sectors?,"Look for: Periodic market reviews or sensitivity analyses in project docs, feedstock cost/supply updates | If absent/vague: No mention of ongoing monitoring | Assess: Unmonitored market shifts can trigger unintended leakage."
+Carbon Accounting & Integrity,Leakage,Supply Chain & Transport Emissions,"Has the project included all transport-related emissions (feedstock to facility, biochar to end user) in net carbon calculations?","Look for: Transport distances, vehicle types, emission factors in the LCA | If absent/vague: Project may omit or assume negligible transport | Assess: Ignoring transport can inflate net removals, hurting credit accuracy."
+Carbon Accounting & Integrity,Leakage,Supply Chain & Transport Emissions,Does the project use local sourcing or low-carbon transport to minimize or offset transportation emissions?,"Look for: Short transport routes, electric/hybrid vehicles, or offset measures | If absent/vague: Only general statements about 'efficient logistics' | Assess: Minimizing/offsetting transport emissions is essential for credible net removals."
+Carbon Accounting & Integrity,Leakage,Monitoring & Mitigation of Leakage,Does the project periodically reassess feedstock sourcing and land use to detect new leakage risks?,Look for: Scheduled reviews or MRV plan updates with land-use or market reassessment | If absent/vague: A static approach with no re-checks | Assess: Regular reassessment helps catch evolving leakage pathways.
+Carbon Accounting & Integrity,Leakage,Monitoring & Mitigation of Leakage,"Are mitigation actions defined if leakage is detected, such as switching feedstock sources or adjusting credit calculations?",Look for: Contingency plans or thresholds that trigger remedial actions | If absent/vague: Only vague promises to 'fix issues' | Assess: Defined strategies reduce overall project leakage risk; their absence increases it.
+Carbon Accounting & Integrity,Leakage,Life Cycle Emissions,"Does the project define a cradle-to-grave boundary and account for infrastructure-related emissions (e.g., new facilities)?",Look for: LCA specifying if construction/installation emissions are included | If absent/vague: Possibly these embedded emissions are omitted | Assess: Omitting facility emissions can overstate net removals.
+Carbon Accounting & Integrity,Leakage,Market Leakage,"Could large-scale biochar production distort local feedstock markets, driving up prices or limiting supply for other low-carbon uses?","Look for: Discussions of the project’s share of local biomass demand, supply constraints, competition | If absent/vague: The project assumes unlimited feedstock without proof | Assess: Market distortions can indirectly cause leakage."
+Carbon Accounting & Integrity,Leakage,Positive Leakage,"Does the project create uncredited GHG reductions or co-benefits (e.g., soil carbon gains, fossil energy displacement)?","Look for: References to side benefits not claimed as credits (e.g., improved soil health, on-site energy use) | If absent/vague: Possibly no mention of co-benefits | Assess: Positive leakage can bolster overall climate impact."
+Carbon Accounting & Integrity,Leakage,Ecological Leakage,"Could changes in biomass extraction or land management shift environmental impacts (e.g., habitat disruption) offsite?","Look for: Ecological impact assessments referencing offsite effects, biodiversity surveys, or water usage changes | If absent/vague: Project does not address broader ecosystem shifts | Assess: Overlooked ecological leakage can undermine net climate benefits."
+Carbon Accounting & Integrity,MRV,Monitoring Systems & Data Quality,"Does the project follow a structured monitoring plan for key parameters (feedstock volume, pyrolysis conditions, biochar output)?","Look for: An MRV protocol specifying frequency, data logging methods, QA processes | If absent/vague: Only a general process description with no measurable metrics | Assess: A robust plan is essential for reliable crediting; ad hoc data collection is risky."
+Carbon Accounting & Integrity,MRV,Monitoring Systems & Data Quality,Is there third-party verification or auditing to confirm reported data accuracy?,"Look for: Independent audits by recognized bodies (Verra, Puro, Riverse) or equivalent | If absent/vague: Only self-reported data with no external checks | Assess: External verification greatly increases trust in data integrity."
+Carbon Accounting & Integrity,MRV,Transparency & Data Accessibility,"Are raw data, methodologies, and assumptions disclosed so stakeholders can review or recalculate carbon claims?","Look for: Detailed LCA reports/appendices with calculation steps, references, and spreadsheets | If absent/vague: Only high-level summaries with no underlying data | Assess: Transparent disclosure enables independent verification; opacity reduces confidence."
+Carbon Accounting & Integrity,MRV,Transparency & Data Accessibility,Does the project maintain historical records to allow trend analysis over multiple reporting periods?,"Look for: Archived annual reports or time-series data for feedstock, energy use, production outputs | If absent/vague: Only current data is shown with no historical context | Assess: Historical records are vital for spotting anomalies or performance trends."
+Carbon Accounting & Integrity,MRV,Long-Term Monitoring & Post-Application Verification,Does the project track biochar stability in its end-use environment (soil or construction materials) over time?,"Look for: Protocols detailing field sampling intervals, measurement methods, or remote sensing for post-application stability | If absent/vague: Project only states application method with no follow-up | Assess: Without tracking, permanence claims are unverified."
+Carbon Accounting & Integrity,MRV,Long-Term Monitoring & Post-Application Verification,Are contingency measures in place if biochar degradation or disturbance is detected after application?,"Look for: Corrective action plans (re-application, extra offsets, operational adjustments) triggered if degradation occurs | If absent/vague: Only a vague promise to 'handle issues' | Assess: Concrete measures reduce the risk of unaddressed carbon loss."
+Carbon Accounting & Integrity,MRV,Periodic Reporting & Methodology Adjustments,Does the project update its MRV methodology based on new research or operational data?,"Look for: Auditor notes or documentation on revised emission factors, baseline models, or sampling methods | If absent/vague: A static MRV approach from inception with no improvements | Assess: Ongoing methodological refinement is key to accurate crediting."
+Carbon Accounting & Integrity,MRV,Periodic Reporting & Methodology Adjustments,Are errors or deviations in reported data transparently corrected and communicated to stakeholders?,"Look for: Revision logs, updated credit issuances, or auditor communications explaining corrections | If absent/vague: Discrepancies may never be addressed or only mentioned in passing | Assess: Transparent error correction builds trust; ignoring it fosters inaccuracies."
+Delivery risk,Supply chain,Feedstock Availability & Security,Has the project secured multi-year agreements with feedstock suppliers specifying volumes and pricing?,Look for: Signed contracts or documented supply commitments in project/audit reports | If absent/vague: Reliance on informal deals or spot markets | Assess: Secure long-term contracts reduce supply risk; lack of them increases the chance of shortages.
+Delivery risk,Supply chain,Feedstock Availability & Security,"Does the project have contingency plans (backup suppliers, buffer stock) for disruptions or price spikes?",Look for: Risk management strategies referencing secondary suppliers or on-site storage | If absent/vague: Project assumes stable supply with no backups | Assess: No contingency means higher vulnerability to feedstock fluctuations.
+Delivery risk,Supply chain,Competing Market Demands & Regulatory Barriers,"Could other industries (bioenergy, pulp/paper) outbid the project for biomass, reducing feedstock availability?",Look for: Market analyses or auditor notes on local biomass demand/competition | If absent/vague: Only claims of 'biomass abundance' with no data | Assess: High competition can raise costs and threaten viability.
+Delivery risk,Supply chain,Competing Market Demands & Regulatory Barriers,"Are emerging regulations (e.g., forest conservation, export bans) likely to constrain feedstock sourcing?","Look for: Policy risk assessments or auditor references to possible regulatory changes | If absent/vague: Project may ignore local legislation trends | Assess: Sudden restrictions can limit access, increasing operational risk."
+Delivery risk,Supply chain,Logistics & Transportation,Is existing transport infrastructure sufficient to handle the required biomass volumes without repeated delays?,"Look for: Documentation on logistics arrangements, route assessments, or transport contracts | If absent/vague: No detail on how raw materials reach the facility | Assess: Poor infrastructure can cause delays, undermining credit delivery schedules."
+Delivery risk,Supply chain,Logistics & Transportation,"Has the project accounted for rising fuel costs or potential disruptions (weather, political instability) in its transport planning?",Look for: Financial sensitivity analyses or risk management plans addressing transport cost fluctuations | If absent/vague: No mention of transport risks | Assess: Unanticipated cost spikes can delay production and increase expenses.
+Delivery risk,Supply chain,Traceability & Transparency,Does the project track the origin of each feedstock batch with chain-of-custody documentation?,"Look for: Digital/paper logbooks, blockchain, or barcode systems referenced in project docs | If absent/vague: Only broad statements about feedstock sourcing | Assess: Strong traceability is vital for verifying sustainable, legal sourcing."
+Delivery risk,Supply chain,Traceability & Transparency,Are third-party audits performed to verify feedstock sustainability and detect illicit harvesting?,"Look for: Certifications (FSC, PEFC) or auditor statements about independent sourcing verification | If absent/vague: Project relies solely on internal claims | Assess: External audits build confidence; their absence raises compliance risks."
+Delivery risk,Equipment & machinery,Equipment Durability & Maintenance,Is the pyrolysis equipment designed for industrial-scale operation with proven durability and a valid warranty?,"Look for: Manufacturer details, historical performance data, or warranty documents in project reports | If absent/vague: Only generic 'robust equipment' claims | Assess: Proven, well-maintained equipment lowers downtime risk."
+Delivery risk,Equipment & machinery,Equipment Durability & Maintenance,Does the project follow a structured preventive maintenance schedule with documented logs?,"Look for: Maintenance manuals, scheduled service intervals, logbooks, or auditor confirmations | If absent/vague: Only reactive repairs are mentioned | Assess: Regular maintenance is crucial for reliable operation."
+Delivery risk,Equipment & machinery,Energy Efficiency & Cost Stability,"Which measures (heat recovery, syngas reuse) reduce external fuel consumption during pyrolysis?","Look for: Process diagrams or technical descriptions of recirculating waste heat or capturing syngas | If absent/vague: Minimal detail on energy optimization | Assess: Efficient energy use lowers costs and emissions, improving feasibility."
+Delivery risk,Equipment & machinery,Energy Efficiency & Cost Stability,Does the project model fuel price sensitivities to ensure profitability and stable credit delivery if energy costs rise?,Look for: Financial scenario analyses or risk management plans addressing rising fuel/electricity costs | If absent/vague: Energy cost assumptions may be fixed with no contingency | Assess: Poor modeling can lead to financial instability.
+Delivery risk,Equipment & machinery,Regulatory Compliance & Safety,Has the project obtained all necessary permits for pyrolysis operations and worker safety?,"Look for: Permit numbers, regulatory approvals, or auditor confirmations of legal compliance | If absent/vague: Only vague 'we comply with standards' statements | Assess: Missing permits or unclear compliance risk shutdowns."
+Delivery risk,Equipment & machinery,Regulatory Compliance & Safety,"Are adequate safety measures (PPE, emergency shutdown systems, gas filtration) documented to protect workers and the environment?","Look for: Specific safety protocols, training records, specs for filtration or alarms | If absent/vague: Generic mentions of 'safe operations' with no details | Assess: Insufficient safety provisions risk accidents and liability."
+Environmental factors,Negative impact,Biodiversity & Habitat Disruption,Does feedstock harvesting or land-use change threaten sensitive ecosystems or protected habitats?,"Look for: Environmental impact assessments, maps, or references to proximity to protected areas | If absent/vague: Only 'sustainable sourcing' with no local ecological detail | Assess: Habitat disruption raises reputational and regulatory risks."
+Environmental factors,Negative impact,Biodiversity & Habitat Disruption,"Has the project implemented offset or conservation measures (e.g., reforestation, wildlife corridors) to mitigate unavoidable habitat disruption?","Look for: Biodiversity offset programs, reforestation initiatives, or conservation partnerships in audit reports | If absent/vague: No mention of remediation for ecological impacts | Assess: Lacking mitigation can harm sustainability profile."
+Environmental factors,Negative impact,Soil Degradation Risks,Does large-scale biomass removal risk depleting soil organic matter or causing erosion in source areas?,"Look for: Harvest management plans, rotation schedules, or soil health assessments | If absent/vague: Project claims 'ample feedstock' with no sustainability data | Assess: Overharvesting can degrade land and jeopardize supply."
+Environmental factors,Negative impact,Soil Degradation Risks,"Has the project tested for potential negative side effects of biochar (e.g., altered soil pH, nutrient lock-up)?",Look for: Field trials or lab tests comparing soil properties before/after application | If absent/vague: Only 'biochar benefits' claimed with no chemical data | Assess: Negative effects can reduce soil productivity and project benefits.
+Environmental factors,Negative impact,Water Resource Depletion & Pollution,"How does the project manage water use (cooling, steam) for pyrolysis to avoid depleting local resources?","Look for: Water usage records, recycling/closed-loop systems, watershed impact assessments | If absent/vague: Only brief mentions of water with no data | Assess: High water usage without mitigation can cause local conflicts or ecological harm."
+Environmental factors,Negative impact,Water Resource Depletion & Pollution,Are wastewater or effluent streams from pyrolysis treated before discharge?,"Look for: Descriptions of on-site treatment systems, test results, or compliance with local discharge standards | If absent/vague: Only generic statements about water disposal | Assess: Untreated effluent risks pollution and regulatory issues."
+Environmental factors,Negative impact,Air Pollution & Harmful Emissions,"Does the pyrolysis process emit particulates, CO, or VOCs, and are emission controls (scrubbers, filters) in place?","Look for: Emission test data, descriptions of gas cleaning, compliance with air quality regulations | If absent/vague: Only 'low emissions' claimed with no technical backup | Assess: Inadequate controls pose health and environmental risks."
+Environmental factors,Negative impact,Air Pollution & Harmful Emissions,"Are feedstocks screened for contaminants that could produce toxic byproducts (e.g., dioxins, heavy metals) during pyrolysis?","Look for: Screening protocols, supplier certifications, or lab tests on incoming biomass | If absent/vague: Only vague claims of 'clean feedstock' | Assess: Contaminated feedstock can lead to harmful emissions and regulatory concerns."
+Environmental factors,Negative impact,Land-Use Change & Agricultural Displacement,"Does the project convert farmland or forests, potentially displacing local communities or food production?",Look for: Land-use impact assessments or stakeholder consultations showing potential displacement | If absent/vague: Project states large-scale ops with no mention of prior land uses | Assess: Unmitigated land conversion can spark socio-economic conflict.
+Environmental factors,Negative impact,Land-Use Change & Agricultural Displacement,Has the project evaluated whether feedstock cultivation intrudes on indigenous lands or smallholder farms?,"Look for: Land tenure studies, FPIC processes, or community meeting records | If absent/vague: No mention of local land rights or indigenous involvement | Assess: Ignoring local rights can lead to legal challenges and conflict."
+Environmental factors,Negative impact,Overextraction of Biomass,Do biomass harvesting rates exceed sustainable regeneration levels in source areas?,"Look for: Harvest management plans, sustainable yield data, or rotation cycles in supplier reports | If absent/vague: 'Ample biomass' claims with no data | Assess: Overharvesting harms ecosystems and reduces long-term viability."
+Environmental factors,Negative impact,Overextraction of Biomass,"Are local communities dependent on the same biomass for fuel or fodder, risking shortages if diverted?","Look for: Socioeconomic studies or stakeholder consultations on local biomass reliance | If absent/vague: No mention of local usage, though rural setting is implied | Assess: Competing uses can cause conflict and undermine net benefit."
+Environmental factors,Negative impact,Heavy Metal & Contaminant Risks,"Is biochar regularly tested for heavy metals (Pb, Cd, Hg) and organic pollutants (PAHs, dioxins) to ensure safety?",Look for: Lab analysis reports showing contaminant concentrations vs. recognized thresholds | If absent/vague: Claims of 'clean biochar' without test data | Assess: Testing is essential to prevent toxic contamination and environmental harm.
+Environmental factors,Negative impact,Heavy Metal & Contaminant Risks,Does the project have protocols for rejecting or remediating biochar batches exceeding contamination limits?,"Look for: SOPs or QA policies detailing corrective actions (e.g., discarding off-spec biochar) | If absent/vague: No mention of remediation if contamination occurs | Assess: Lack of a plan raises risk of polluted biochar use."
+Environmental factors,Negative impact,Noise Pollution & Community Disruption,"Has the project measured machinery noise levels and implemented mitigation (e.g., acoustic barriers, scheduling)?",Look for: Noise impact assessments with decibel readings and stated mitigation steps | If absent/vague: Only 'we are quiet' with no data | Assess: Excessive noise can lead to community complaints or legal action.
+Environmental factors,Negative impact,Noise Pollution & Community Disruption,Is there a grievance mechanism for local residents to report noise or other disturbances?,"Look for: Complaint hotlines, community liaison officers, or formal dispute resolution processes | If absent/vague: Only broad mentions of 'good community relations' | Assess: No feedback channel can lead to unresolved tensions."
+Environmental factors,Negative impact,Negative Socioeconomic Externalities,Could the project displace existing livelihoods or disrupt local economic activities?,"Look for: Socioeconomic impact assessments, stakeholder consultations, or auditor notes on local businesses/employment | If absent/vague: Only emphasis on job creation with no mention of displacement | Assess: Disruption can cause community resistance."
+Environmental factors,Negative impact,Negative Socioeconomic Externalities,Are labor conditions and wages in line with ethical and legal standards?,"Look for: Documentation of compliance with local labor laws, fair wage policies, worker safety measures | If absent/vague: Generic mentions of 'good labor practices' without detail | Assess: Poor labor conditions pose ethical, legal, and reputational risks."
+Environmental factors,Co-benefits,Improved Soil Health & Productivity,"Has the project measured increases in soil organic matter, CEC, or crop yields after biochar application?","Look for: Field trial data, before/after studies, or audit reports with numerical comparisons | If absent/vague: Only anecdotal or general statements | Assess: Quantitative improvements validate claims of soil health co-benefits."
+Environmental factors,Co-benefits,Improved Soil Health & Productivity,Is there evidence that biochar enhances soil water retention or drought resilience?,"Look for: Studies showing reduced irrigation needs, higher soil moisture metrics post-biochar | If absent/vague: Only broad statements of 'better water holding' | Assess: Documented gains strengthen co-benefit claims."
+Environmental factors,Co-benefits,Water Conservation & Quality,"Does biochar reduce nutrient leaching or runoff, improving nearby water quality?",Look for: Comparative water quality data (nitrate/phosphate levels) before and after application | If absent/vague: Only unquantified claims of 'less runoff' | Assess: Measurable improvements are a strong co-benefit.
+Environmental factors,Co-benefits,Water Conservation & Quality,Have documented decreases in irrigation demand occurred due to biochar application?,Look for: Farm records or scientific reports showing reduced water usage per hectare | If absent/vague: Only 'we save water' claims with no data | Assess: Quantified evidence underscores real water conservation benefits.
+Environmental factors,Co-benefits,Air Quality Improvement,Does converting biomass to biochar reduce particulates or pollutant emissions vs. open burning or decay?,Look for: LCA comparisons or emission test data contrasting pyrolysis with typical disposal methods | If absent/vague: Only 'reduces smoke' claims without specifics | Assess: Verified emission reductions bolster air quality co-benefits.
+Environmental factors,Co-benefits,Air Quality Improvement,Has the project measured reductions in methane or other GHGs compared to unmanaged biomass decomposition?,Look for: Quantitative CH₄ emission comparisons from baseline disposal vs. pyrolysis outputs | If absent/vague: Only qualitative claims about 'less methane' | Assess: Documented CH₄ reduction is a strong co-benefit.
+Environmental factors,Co-benefits,Biodiversity Protection & Habitat Restoration,"Does the project integrate biochar into agroforestry or land restoration, enhancing native vegetation or soil biodiversity?","Look for: Reforestation programs, agroforestry pilots, or biodiversity monitoring data | If absent/vague: Only broad ecological benefit claims without site evidence | Assess: Active land restoration can add significant co-benefits."
+Environmental factors,Co-benefits,Biodiversity Protection & Habitat Restoration,Have pollinator or beneficial insect populations increased where biochar is applied?,Look for: Biodiversity surveys or ecological studies noting higher pollinator/fauna abundance | If absent/vague: Only 'improved ecology' claims with no data | Assess: Measurable increases in key species support biodiversity co-benefits.
+Environmental factors,Co-benefits,Waste Management & Circular Economy,"Does the project use true waste or residue feedstocks, diverting them from landfills or open dumping?",Look for: References to agricultural/forestry residues otherwise destined for disposal | If absent/vague: Vague 'waste biomass' with no proof of disposal methods | Assess: Genuine waste usage enhances circular economy benefits.
+Environmental factors,Co-benefits,Waste Management & Circular Economy,"Are pyrolysis byproducts (syngas, bio-oil) captured and utilized, improving resource efficiency?",Look for: Process flow diagrams or financial reports detailing byproduct usage or sale | If absent/vague: Byproducts may be flared or discarded | Assess: Utilizing byproducts strengthens circular economy claims.
+Environmental factors,Co-benefits,Regenerative Agriculture & Sustainable Land Management,"Is biochar applied alongside regenerative practices (cover cropping, no-till) to enhance soil carbon sequestration?",Look for: Project/audit reports describing synergy with other soil-building methods | If absent/vague: Only mentions biochar application with no broader management context | Assess: Combined approaches amplify agronomic and climate benefits.
+Environmental factors,Co-benefits,Regenerative Agriculture & Sustainable Land Management,Have reductions in synthetic fertilizer or pesticide use been documented due to improved nutrient retention?,Look for: Farmer testimonials or comparative data showing decreased agrochemical usage post-biochar | If absent/vague: Claims of 'less fertilizer' without quantification | Assess: Verified reductions yield both economic and environmental co-benefits.
+Environmental factors,Co-benefits,Community Benefits & Local Development,"Does the project create local jobs, training programs, or revenue-sharing mechanisms for surrounding communities?","Look for: Employment figures, training workshop descriptions, documented revenue-sharing policies | If absent/vague: Only vague 'community benefits' statements | Assess: Concrete local economic uplift fosters social acceptance and sustainability."
+Environmental factors,Co-benefits,Community Benefits & Local Development,"Are underrepresented groups (women, youth, indigenous) included in decision-making or capacity-building efforts?","Look for: Demographic data on workforce or training, explicit inclusion policies, targeted outreach | If absent/vague: Only general references to 'community involvement' | Assess: Inclusive engagement improves equity and project resilience."
+Social impact,Economic empowerment,Job Creation & Fair Labor,"Does the project provide stable, fairly compensated jobs with safe working conditions, prioritizing local hires?","Look for: Wage levels above legal minimum, local recruitment policies, safety measures (PPE/training) in audit reports | If absent/vague: Only mentions 'local hires' without detail on wages or safety | Assess: Fair employment is key for social credibility."
+Social impact,Economic empowerment,Job Creation & Fair Labor,Is there a formal system for worker grievances and independent verification of labor rights compliance?,"Look for: Grievance procedures, worker hotlines, union representation, or third-party labor audits | If absent/vague: Only generic claims of good labor practices | Assess: A robust system reduces exploitation risk."
+Social impact,Economic empowerment,Local Business & Entrepreneurship Support,"Are local smallholders or cooperatives integrated into the biochar value chain (e.g., feedstock sales, distribution partnerships)?",Look for: Contracts or partnership agreements with local farmers or co-ops in project/audit docs | If absent/vague: Only external operators manage the value chain | Assess: Local integration supports broader economic growth.
+Social impact,Economic empowerment,Local Business & Entrepreneurship Support,"Does the project offer financial or technical support (microfinance, training) to local entrepreneurs developing biochar-related businesses?","Look for: Mentions of funding programs, loan facilities, or equipment leasing for local enterprises | If absent/vague: No mention of direct support mechanisms | Assess: Fostering local entrepreneurship multiplies community benefits."
+Social impact,Economic empowerment,Income Diversification & Economic Resilience,"Can local stakeholders diversify income (e.g., carbon credits, improved crop yields) rather than relying on a single source?",Look for: Evidence of multiple revenue channels or success stories from farmers increasing income streams | If absent/vague: Only a single income approach is described | Assess: Diversification boosts resilience against market shocks.
+Social impact,Economic empowerment,Income Diversification & Economic Resilience,Has the project analyzed long-term market demand for biochar or carbon credits so participants aren’t exposed if conditions change?,Look for: Scenario planning or market analyses referencing future price/demand fluctuations | If absent/vague: No discussion of potential volatility | Assess: Proactive planning supports sustainable local incomes over time.
+Social impact,Economic empowerment,Equitable Benefit-Sharing,"Are project revenues (e.g., from carbon credits) transparently allocated so workers and communities get a fair share?",Look for: Financial breakdowns or revenue-sharing agreements in project/audit docs | If absent/vague: Distribution of benefits is not clearly specified | Assess: Transparency fosters trust; opacity may lead to perceptions of inequity.
+Social impact,Economic empowerment,Equitable Benefit-Sharing,"Do marginalized groups (women, indigenous) have structured access to economic benefits from the project?","Look for: Policies, quotas, or targeted programs for inclusive participation | If absent/vague: Only broad statements about benefiting 'the community' | Assess: Without explicit measures, benefits might be unevenly distributed."
+Social impact,Economic empowerment,Capacity Building & Skills Development,"Does the project provide formal training or apprenticeships in biochar production, equipment maintenance, or sustainable land management?","Look for: Training curricula, workshop logs, or documented apprenticeship programs | If absent/vague: Only general mentions of 'offering training' with no structure | Assess: Formal programs build long-term local capacity."
+Social impact,Economic empowerment,Capacity Building & Skills Development,Are less-skilled or underrepresented workers able to advance into technical or managerial roles?,"Look for: Career progression initiatives, advanced courses, or promotion pathways in project docs | If absent/vague: Only entry-level positions are mentioned | Assess: Opportunities for advancement improve economic resilience."
+Social impact,Economic empowerment,Infrastructure & Economic Spillovers,"Has the project invested in local infrastructure (roads, energy access) that benefits the wider community?",Look for: Auditor references or project docs on improvements beyond the project site | If absent/vague: Only internal infrastructure is mentioned | Assess: Community-level investments amplify local economic impacts.
+Social impact,Economic empowerment,Infrastructure & Economic Spillovers,Are indirect benefits for local suppliers or service providers documented as a result of project operations?,"Look for: Evidence of increased business activity, new supply chain partnerships, or economic reports on spillovers | If absent/vague: Only direct employment is cited | Assess: Positive indirect impacts indicate a multiplier effect on the local economy."
+Policy,Implementation & Enforcement,Government Effectiveness,Are there well-defined legislative frameworks or incentives that support biochar projects at the national or local levels?,"Look for: References to specific climate policies, negative emissions regulations, or stable carbon markets | If absent/vague: Only general statements about climate commitments | Assess: Strong policy frameworks reduce risk; unclear frameworks raise uncertainty."
+Policy,Implementation & Enforcement,Government Effectiveness,"How reliably are these policies enforced, and do they consistently support biochar project implementation?","Look for: Government or auditor reports on enforcement track records, regulatory oversight, or success rates | If absent/vague: Policy details might exist but enforcement is unknown | Assess: Weak enforcement raises viability risks."
+Policy,Implementation & Enforcement,Government Effectiveness,"Does the government allocate stable financial resources (subsidies, climate finance) to support biochar projects?","Look for: Mention of public grants, budget lines, or carbon credit purchasing commitments | If absent/vague: Possible ad-hoc or pilot funding only | Assess: Stable, long-term funding reduces policy risk and fosters project continuity."
+Policy,Implementation & Enforcement,Government Effectiveness,Could strong government support reduce the project’s additionality by making biochar deployment likely anyway?,"Look for: Policies or subsidies that mandate or heavily incentivize biochar at scale without carbon finance | If absent/vague: No direct mention of government-led expansions | Assess: If policy alone drives adoption, additionality is weaker."
+Policy,Policy & Market Context,Green Investment Landscape,Does the country have explicit green investment or climate finance policies that favor biochar or carbon removal projects?,"Look for: National climate strategies, carbon pricing, or finance programs referencing biochar or negative emissions | If absent/vague: Only generic net-zero pledges with no practical instruments | Assess: Strong frameworks support scaling; lack thereof hinders investment."
+Policy,Policy & Market Context,Green Investment Landscape,Is the level of green investment activity in this country sufficient to create a supportive ecosystem for biochar?,"Look for: Data on climate finance flows, VC for sustainability, or foreign investments in carbon removal | If absent/vague: Only broad 'government interest' claims | Assess: High investment activity lowers policy risk; limited activity is a hurdle."
+Policy,Policy & Market Context,Green Investment Landscape,"Are there competing carbon removal projects (BECCS, DAC, afforestation) that affect demand or funding for biochar?","Look for: References to alternative removal methods, government or private sector preferences, or shared feedstock supplies | If absent/vague: Project may ignore competition for resources/funding | Assess: Multiple technologies can split limited climate finance."
+Policy,Policy & Market Context,Green Investment Landscape,"What key risks (policy instability, market volatility) exist for biochar investment in this country?","Look for: Past policy reversals, macroeconomic instability, corruption indices in project/audit docs | If absent/vague: Only broad 'we see some risks' references | Assess: High volatility undermines long-term viability."
+Policy,Policy & Market Context,Green Investment Landscape,Is the biochar project financially viable and scalable given the local/national investment context?,"Look for: Feasibility studies, ROI analyses, synergy with local industries (agriculture, biomass) | If absent/vague: Only broad positivity with no data | Assess: Lack of proven viability signals high risk; robust evidence fosters confidence."
+Policy,Policy & Market Context,Major Policies,"Are there major national/regional/international policies specifically supporting biochar or carbon removal, and do they align with the project?","Look for: Cited laws, climate targets, or carbon pricing mechanisms that reference biochar adoption | If absent/vague: Project may not leverage existing policy tools | Assess: Alignment can boost viability; misalignment can add regulatory risk."
+Policy,Policy & Market Context,Major Policies,"Have these policies been effectively implemented, or is there a history of slow/uneven enforcement in this sector?",Look for: Auditor/third-party reports on policy implementation success or failures | If absent/vague: Policy enforcement remains unclear | Assess: Weak enforcement undermines reliability of policy support.
+Policy,Policy & Market Context,Major Policies,"Do different policy levels (local, national) conflict or create regulatory uncertainty for biochar deployment?","Look for: Evidence of contradictory rules (e.g., local bans vs. national incentives) or misaligned frameworks | If absent/vague: No mention of multi-level regulation synergy | Assess: Overlapping or clashing regulations add risk."
+Policy,Policy & Market Context,Nationally Determined Contributions,Does the host country’s NDC mention or prioritize biochar or engineered carbon removals?,Look for: References in official NDC submissions or alignment with the project approach | If absent/vague: Biochar might not be part of national strategy | Assess: Inclusion in NDC can support policy but reduce additionality if it becomes mandated.
+Policy,Policy & Market Context,Nationally Determined Contributions,"If biochar is in the NDC, is the government likely to implement such projects without carbon finance?",Look for: Government-led programs or funding for large-scale biochar expansions | If absent/vague: The NDC mention might be aspirational with no real path | Assess: Strong government action can lessen credit-based additionality.
+Policy,Policy & Market Context,Nationally Determined Contributions,"Has the country historically met or missed NDC targets, and might that affect reliance on private carbon projects like biochar?",Look for: External climate performance reports or references to successes/failures in meeting targets | If absent/vague: Only 'ongoing efforts' are mentioned | Assess: Missed targets can boost reliance on private initiatives; consistent success might reduce the impetus.
+Policy,Policy & Market Context,Nationally Determined Contributions,Could evolving NDC commitments or policy changes alter the regulatory environment for biochar over time?,Look for: Upcoming policy revisions or new climate goals that might mandate or shift focus away from voluntary credits | If absent/vague: No mention of future NDC updates | Assess: Rapid policy shifts can create uncertainty or reduce voluntary credit demand.
+Policy,Policy & Market Context,Host Country Classification & Economic Context,"How is the host country classified economically (LDC, low/middle/high-income), and does this affect policy frameworks or investment conditions for biochar?","Look for: Official classification by the UN or World Bank, or references to GDP/income levels | If absent/vague: Only broad mentions of 'developing country' | Assess: Lower-income nations may rely on external finance; wealthier contexts may have robust domestic funding."
+Policy,Policy & Market Context,Host Country Classification & Economic Context,"Do infrastructure gaps, regulatory weaknesses, or limited financing common to lower-income settings pose extra risks to the project?",Look for: Acknowledgments of capacity constraints or logistical barriers in the project’s risk analysis | If absent/vague: No mention of structural hurdles | Assess: Unaddressed constraints can undermine project viability.
+Policy,Policy & Market Context,Host Country Classification & Economic Context,Could a higher-income country classification undermine additionality by providing alternative public funding or mandates?,"Look for: Evidence of robust government budgets, mandated negative emissions, or strong subsidies overshadowing carbon credit needs | If absent/vague: No mention of large-scale government climate finance | Assess: Where public funds exist, credit finance might be less crucial."
+Policy,Policy & Market Context,Host Country Classification & Economic Context,How does the national classification influence regulatory stability or market confidence for biochar projects?,"Look for: References to corruption indices, business environment ratings, or historical policy fluctuations | If absent/vague: Only general references to 'policy environment' | Assess: Unstable or corrupt environments increase policy risk, while stable contexts lower it."
diff --git a/sequestrae_engine/document_parsing/prompts/due_diligence_system_prompt_current_project.txt b/sequestrae_engine/document_parsing/prompts/due_diligence_system_prompt_current_project.txt
new file mode 100644
index 0000000..8d54c02
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/due_diligence_system_prompt_current_project.txt
@@ -0,0 +1,89 @@
+**Context**
+
+You are a senior **expert in biochar projects**, with deep knowledge of:
+- **Biochar production methods**
+- **Carbon market dynamics**
+- **Lifecycle analysis (LCA)**
+- **Sustainability certifications**
+- **Monitoring, Reporting, and Verification (MRV)**
+
+The provided Markdown content is a concatenation of several documents of a biochar project, which mainly includes project description and (potentially multiple) certification audit reports. Note that audit reports are typically produced annually for projects that have been operating for some time, so the documents may span different periods. This is important for evaluating trends and improvements (or the lack thereof) over time.
+
+The Markdown content is formatted as follows:
+```markdown
+# The following text is the content of file: file1.pdf
+
+[content of file1]
+
+# The following text is the content of file: file2.pdf
+
+[content of file2]
+
+...
+
+```
+
+Your role is to conduct a thorough due diligence review of the project documents for a biochar production project. Your expertise analysis will inform corporate buyers (ESG team) about the project’s potential and various risks (e.g., climate science, delivery risk, environmental aspects, social impact, and policy) based solely on the criteria provided in the “Criteria Guideline” (which will be supplied separately).
+
+Important:
+* As each project is already certified under a specific standard (e.g., Puro, Riverse, Isometric etc.), leverage your expertise and knowledge of that standard’s requirements and typical practices. Where the documentation is incomplete or ambiguous, you may carefully infer or assume common standard practices—but be explicit when doing so.
+* Some questions might not be applicable to the current project. For example, if the biochar is integrated in cement, then the questions about soil health, Regenerative Agriculture etc. are not applicable. As an expert of the domain, think critically before answering each question. Always ask yourself whether the question is valid in the scope of the current project. If a question is not applicable to the project, explicitly state that it is not applicable and justify why.
+* Never return an empty result, you must always return a JSON with the schema as defined below.
+
+
+**Objective**
+
+Evaluate the project solely against the criteria defined in the “Criteria Guideline.” Do not reference or assess any criteria not explicitly included in that guideline. Your task is to identify risks and information gaps in the documents for each criterion and to provide clear, actionable insights for potential buyers.
+
+**Methodology**
+	1.	Locate Relevant Information
+	* Carefully parse the entire Markdown text to extract all relevant details addressing each criterion.
+	* Carefully identify and note the file names or document identifiers in which the relevant evidence is found, as this will help end users to quickly trace back and verify the source information.
+	2.	Assess Each Criterion: For each criterion in the guideline, carefully perform step by step the following steps:
+	* Understand the Question: analyze precisely what the criterion is asking.
+	* Extract Evidence: locate all relevant statements, data, or summaries in the documents that address the criterion, and specify the file(s) where this evidence is found.
+	* Apply Relevant Certification Knowledge: if the documents reference specific standards (e.g., Puro, Verra, Riverse etc.), use your knowledge of that standard’s typical requirements and definitions to contextualize the evidence. Where the documentation is incomplete or unclear, make careful assumptions grounded in standard practice. Note that these are assumptions and highlight any lack of clarity.
+	* Report Findings: for each criterion, judge carefully about whether the question makes sense given the project's context, then provide the following fields:
+		*   `short_answer`: A brief, pertinent summary that directly answers the criterion.
+		*	`long_answer`: The most important field: A detailed, complete explanation and clear reasoning analyzing all relevant insights, including any observed trends or changes across different audit periods. If a specific certification standard is mentioned, incorporate any relevant context or assumptions.
+		*	`detail_level`: Categorize how completely the documentation addresses the criterion.
+			*	"Fully answered": The information is comprehensive and clear.
+			*	"Partially Answered": Some relevant information is provided, but it lacks clarity or completeness.
+			*	"Inconclusive": Insufficient evidence is available to fully answer the criterion.
+		* 	`missing_data`: Specify any important, concrete data or evidence that is missing from the documents and would be required to fully answer the criterion. List specific data sources or types of evidence that need to be collected externally, or return null/an empty string if none. The goal is to help the users know concretely what to look for and where to look for.
+	•	* 	`contradictory_data`: Identify any concrete instances of contradictory information present in the documents that address the criterion. Provide specific details (e.g., conflicting file references or data points), or return null/an empty string if none.
+	3.	Stay Focused:
+	*	Analyze only the criteria provided in the “Criteria Guideline.”
+	*	Do not include or analyze any additional topics beyond those specified.
+	4.	Avoid Redundancy
+	*	Ensure that each criterion’s analysis is precise, pertinent and clear without repeating information unnecessarily.
+
+**Output Format**
+Produce a single **valid JSON array**, each entry corresponding to one criterion from the guideline. Output only the JSON array with no additional text or commentary. The JSON array must adhere exactly to the structure below:
+
+```json
+[
+	{
+		"topic": "Name of the topic exactly as listed in the guideline",
+		"sub_topic": "Name of the subtopic exactly as listed in the guideline",
+		"risk_factor": "Name of the criteria risk factor exactly as listed in the guideline",
+		"question": "Question that the criterion looks to answer as listed in the guideline",
+		"short_answer": "A short, pertinent summary of the finding.",
+		"long_answer": "A detailed, complete explanation and clear reasoning analyzing all relevant insights based on the information retrieved or inferred from the documents, including references to file names or document identifiers where the evidence is found. Incorporate any standard-specific context here if applicable.",
+		"detail_level": "Fully answered | Partially Answered | Inconclusive (choose one)",
+		"missing_data": "Specify all specific necessary concrete data and information that are missing and should be collected from external sources, or null/empty string if none.",
+		"contradictory_data": "Specify any concrete contradictory information present in the documents, or null/empty string if none."
+	},
+	{
+		...
+	},
+    // ... additional criteria objects
+]
+```
+
+**Final Instructions**
+	* Thorough Parsing & Analysis: Read the entire Markdown audit report carefully, ensuring every segment is evaluated against the criteria.
+	* Strict JSON Output: Your final output must consist exclusively of the valid JSON array specified above, with no additional commentary or text.
+	* DO NOT return an empty list ([]).
+	* Make sure that there is no syntax/format error with the json output.
+	* Actionable Insights: Provide precie, clear, pertinent and actionable insights that help buyers assess the project’s performance, including trends over time if multiple audit documents are present. Leverage your knowledge of specific certification standards (e.g., Puro, Riverse, Isometric etc.) responsibly and make careful assumptions only where necessary.
diff --git a/sequestrae_engine/document_parsing/prompts/due_diligence_system_prompt_future_project.txt b/sequestrae_engine/document_parsing/prompts/due_diligence_system_prompt_future_project.txt
new file mode 100644
index 0000000..c31830b
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/due_diligence_system_prompt_future_project.txt
@@ -0,0 +1,91 @@
+**Context**
+
+You are an **expert in biochar projects**, with deep knowledge of:
+- **Biochar production methods**
+- **Carbon market dynamics**
+- **Lifecycle analysis (LCA)**
+- **Sustainability certifications**
+- **Monitoring, Reporting, and Verification (MRV)**
+
+The provided Markdown content is a concatenation of several documents of a biochar project, which mainly includes project description and (potentially multiple) certification audit reports. Note that audit reports are typically produced annually for projects that have been operating for some time, so the documents may span different periods. This is important for evaluating trends and improvements (or the lack thereof) over time.
+
+The Markdown content is formatted as follows:
+```markdown
+# The following text is the content of file: file1.pdf
+
+[content of file1]
+
+# The following text is the content of file: file2.pdf
+
+[content of file2]
+
+...
+
+```
+
+Your role is to conduct a thorough due diligence review of the project documents for a biochar production project. Your analysis will inform corporate buyers (ESG team) about the project’s potential and various risks (e.g., climate science, delivery risk, environmental aspects, social impact, and policy) based solely on the criteria provided in the “Criteria Guideline” (which will be supplied separately).
+
+Important:
+* As each project is already certified under a specific standard (e.g., Puro, Riverse, Isometric etc.), leverage your expertise and knowledge of that standard’s requirements and typical practices. Where the documentation is incomplete or ambiguous, you may carefully infer or assume common standard practices—but be explicit when doing so.
+* Some questions might not be applicable to the current project. For example, if the biochar is integrated in cement, then the questions about soil health, Regenerative Agriculture etc. are not applicable. As an expert of the domain, think critically before answering each question. Always ask yourself whether the question is valid in the scope of the current project. If a question is not applicable to the project, explicitly state that it is not applicable and justify why.
+* Never return an empty result, you must always return a JSON with the schema as defined below.
+
+
+**Objective**
+
+Evaluate the project solely against the criteria defined in the “Criteria Guideline.” Do not reference or assess any criteria not explicitly included in that guideline. Your task is to identify risks and information gaps in the documents for each criterion and to provide clear, actionable insights for potential buyers.
+
+The biochar project under review has not yet begun production but is scheduled to start in the near future. Consequently, there may be little or no historical or operational data available. Please factor this into your evaluation: if certain criteria questions assume ongoing or past operations (e.g., performance metrics), critically assess whether they are applicable or note that they cannot be answered meaningfully at this stage. It may be normal for reports to omit certain historical performance data if operations have not begun, so avoid treating missing operational details as a deficiency unless the “Criteria Guideline” explicitly requires them.
+
+**Methodology**
+	1.	Locate Relevant Information
+	* Carefully parse the entire Markdown text to extract all relevant details addressing each criterion.
+	* Carefully identify and note the file names or document identifiers in which the relevant evidence is found, as this will help end users to quickly trace back and verify the source information.
+	2.	Assess Each Criterion: For each criterion in the guideline, carefully perform step by step the following steps:
+	* Understand the Question: analyze precisely what the criterion is asking.
+	* Extract Evidence: locate all relevant statements, data, or summaries in the documents that address the criterion, and specify the file(s) where this evidence is found.
+	* Apply Relevant Certification Knowledge: if the documents reference specific standards (e.g., Puro, Verra, Riverse etc.), use your knowledge of that standard’s typical requirements and definitions to contextualize the evidence. Where the documentation is incomplete or unclear, make careful assumptions grounded in standard practice. Note that these are assumptions and highlight any lack of clarity.
+	* Report Findings: for each criterion, judge carefully about whether the question makes sense given the project's context, then provide the following fields:
+		*   `short_answer`: A brief, pertinent summary that directly answers the criterion.
+		*	`long_answer`: The most important field: A detailed, complete explanation and clear reasoning analyzing all relevant insights, including any observed trends or changes across different audit periods. If a specific certification standard is mentioned, incorporate any relevant context or assumptions.
+		*	`detail_level`: Categorize how completely the documentation addresses the criterion.
+			*	"Fully answered": The information is comprehensive and clear.
+			*	"Partially Answered": Some relevant information is provided, but it lacks clarity or completeness.
+			*	"Inconclusive": Insufficient evidence is available to fully answer the criterion.
+		* 	`missing_data`: Specify any important, concrete data or evidence that is missing from the documents and would be required to fully answer the criterion. List specific data sources or types of evidence that need to be collected externally, or return null/an empty string if none. The goal is to help the users know concretely what to look for and where to look for.
+	•	* 	`contradictory_data`: Identify any concrete instances of contradictory information present in the documents that address the criterion. Provide specific details (e.g., conflicting file references or data points), or return null/an empty string if none.
+	3.	Stay Focused:
+	*	Analyze only the criteria provided in the “Criteria Guideline.”
+	*	Do not include or analyze any additional topics beyond those specified.
+	4.	Avoid Redundancy
+	*	Ensure that each criterion’s analysis is precise, pertinent and clear without repeating information unnecessarily.
+
+**Output Format**
+Produce a single **valid JSON array**, each entry corresponding to one criterion from the guideline. Output only the JSON array with no additional text or commentary. The JSON array must adhere exactly to the structure below:
+
+```json
+[
+	{
+		"topic": "Name of the topic exactly as listed in the guideline",
+		"sub_topic": "Name of the subtopic exactly as listed in the guideline",
+		"risk_factor": "Name of the criteria risk factor exactly as listed in the guideline",
+		"question": "Question that the criterion looks to answer as listed in the guideline",
+		"short_answer": "A short, pertinent summary of the finding.",
+		"long_answer": "A detailed explanation analyzing all relevant insights based on the information retrieved or inferred from the documents, including references to file names or document identifiers where the evidence is found. Incorporate any standard-specific context here if applicable.",
+		"detail_level": "Fully answered | Partially Answered | Inconclusive (choose one)",
+		"missing_data": "Specify all specific necessary concrete data and information that are missing and should be collected from external sources, or null/empty string if none.",
+		"contradictory_data": "Specify any concrete contradictory information present in the documents, or null/empty string if none."
+	},
+	{
+		...
+	},
+    // ... additional criteria objects
+]
+```
+
+**Final Instructions**
+	* Thorough Parsing & Analysis: Read the entire Markdown audit report carefully, ensuring every segment is evaluated against the criteria.
+	* Strict JSON Output: Your final output must consist exclusively of the valid JSON array specified above, with no additional commentary or text.
+	* DO NOT return an empty list ([]).
+	* Make sure that there is no syntax/format error with the json output.
+	* Actionable Insights: Provide precie, clear, pertinent and actionable insights that help buyers assess the project’s performance, including trends over time if multiple audit documents are present. Leverage your knowledge of specific certification standards (e.g., Puro, Riverse, Isometric etc.) responsibly and make careful assumptions only where necessary.
diff --git a/sequestrae_engine/document_parsing/prompts/hallucination_evaluation_prompt.txt b/sequestrae_engine/document_parsing/prompts/hallucination_evaluation_prompt.txt
new file mode 100644
index 0000000..3e36920
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/hallucination_evaluation_prompt.txt
@@ -0,0 +1,91 @@
+**Context**
+
+You are a **biochar expert** with deep knowledge of:
+- **Biochar production methods**
+- **Carbon market dynamics**
+- **Lifecycle analysis (LCA)**
+- **Sustainability certifications: Puro, Verra, Riverse, etc.**
+- **Monitoring, Reporting, and Verification (MRV)**
+
+You have been provided with:
+1. A concatenated Markdown document (the “CONTEXT”) that describes the biochar project, its audits, and any related information.
+2. A JSON array containing multiple criteria/questions related to the same **topic, sub-topic, and risk factor**. Each JSON object has the following structure:
+
+```json
+[
+  {
+    "topic": "Topic from the guideline",
+    "sub_topic": "Sub-topic from the guideline",
+    "risk_factor": "Risk factor from the guideline",
+    "question": "The question from the guideline",
+    "short_answer": "A brief summary of the findings",
+    "long_answer": "A detailed explanation",
+    "detail_level": "Fully answered | Partially Answered | Inconclusive",
+    "missing_data": "Specify all specific necessary concrete data and information that are missing and should be collected from external sources, or null/empty string if none.",
+		"contradictory_data": "Specify any concrete contradictory information present in the documents, or null/empty string if none."
+  },
+  {
+    ...
+  },
+  ...
+]
+```
+
+The answers have been provided by a Large Language Model (LLM). Your objective is to verify that there is no hallucination in these answers.
+
+**Your Task**
+  1. Check the `detail_level` field:
+    •	If "detail_level": "Inconclusive", do not evaluate for hallucinations.
+    •	Instead, return the JSON unchanged, adding the following empty fields:
+    ```json
+    "is_hallucination": "",
+    "hallucination_reasoning": ""
+    ```
+  2.  Read the CONTEXT (the Markdown text) thoroughly.
+  3.  Carefully examine and analyze one by one the list of criteria/questions and their associated answers (`short_answer`, `long_answer`, `missing_or_contradictory_data`).
+  4.  One by one, determine thoroughly if each answer is faithful to the content of the CONTEXT (ie. the LLM did not hallucinate and the end user can trust the results):
+  * A faithful answer:
+    * Derives information **explicitly** from the CONTEXT.
+    * Includes **clearly stated assumptions** based on recognized standards (e.g., Puro, Verra, Riverse).
+    * Assumptions are acceptable only if **explicitly** labeled as such and consistent with how the standard is typically applied.
+  * An unfaithful (hallucinated) answer:
+    * Contains details not mentioned or implied by the CONTEXT and not clearly justified by standard-based assumptions.
+    * Contradicts the CONTEXT.
+    * Fails to label or justify additional details as assumptions when they are not explicitly supported by the CONTEXT.
+  5.  Return a Judgment for Each Entry in the JSON Array:
+  • If an answer contains unsupported or contradicting information, mark it as a hallucination.
+  • If an answer is consistent with the CONTEXT and/or includes validly labeled assumptions, mark it as not a hallucination.
+  6.  Return the updated JSON array that contains all the criteria in the input JSON, do not return undone JSON array.
+**Output Format**
+
+Your output JSON array must have the same format as the input JSON array, but with two new fields added to each entry:
+```json
+[
+    {
+      "topic": "Topic from the guideline",
+      "sub_topic": "Sub-topic from the guideline",
+      "risk_factor": "Risk factor from the guideline",
+      "question": "The question from the guideline",
+      "short_answer": "A brief summary of the findings",
+      "long_answer": "A detailed explanation",
+      "detail_level": "Fully answered | Partially Answered | Inconclusive",
+      "missing_data": "Specify all specific necessary concrete data and information that are missing and should be collected from external sources, or null/empty string if none.",
+		  "contradictory_data": "Specify any concrete contradictory information present in the documents, or null/empty string if none."
+      "is_hallucination": True or False,
+      "hallucination_reasoning": "If hallucination is True, explain why; otherwise an empty string."
+    },
+    {
+      ...
+    },
+]
+```
+**Instructions for the LLM**
+  * Strictly Follow the JSON array Output Format: Return the same list structure with the two additional fields for each entry.
+  * Make sure that the output is a **valid JSON array**, that there is no syntax error.
+  * Make sure that the output is complete: all the criteria in the input JSON array is included and evaluated.
+  * Special Handling for "detail_level": "Inconclusive":
+	  * Do not evaluate hallucination.
+	  * Return "is_hallucination": "" and "hallucination_reasoning": "".
+  * Be Precise and Pertinent: If marking an answer as a hallucination (is_hallucination: True), provide specific reasoning in hallucination_reasoning (e.g., “Mentions financial barriers not found in CONTEXT.”).
+  * Valid Assumptions Are Allowed: If an answer includes clearly stated assumptions based on recognized standards (e.g., Puro), it is not a hallucination.
+  * No Additional Text: Return only the updated JSON array.
diff --git a/sequestrae_engine/document_parsing/prompts/hallucination_fixing_prompt.txt b/sequestrae_engine/document_parsing/prompts/hallucination_fixing_prompt.txt
new file mode 100644
index 0000000..56a49b2
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/hallucination_fixing_prompt.txt
@@ -0,0 +1,130 @@
+**Context**
+
+You are an **expert in biochar projects**, with deep knowledge of:
+- **Biochar production methods**
+- **Carbon market dynamics**
+- **Lifecycle analysis (LCA)**
+- **Sustainability certifications (e.g., Puro, Riverse, Isometric, Verra)**
+- **Monitoring, Reporting, and Verification (MRV)**
+
+The provided Markdown content is a **concatenation of project documents**, including **project descriptions and multiple certification audit reports**.
+Audit reports are typically produced **annually**, meaning the documents **span different periods**. This is important for evaluating trends and improvements **(or the lack thereof)** over time.
+
+The Markdown content is formatted as follows:
+```markdown
+# The following text is the content of file: file1.pdf
+
+[content of file1]
+
+# The following text is the content of file: file2.pdf
+
+[content of file2]
+
+...
+
+Your role is to conduct a thorough due diligence review of the project documents. Your analysis will help potential buyers assess the project’s potential and risks (e.g., climate science, delivery risk, environmental aspects, social impact, policy), based solely on the criteria provided in the “Criteria Guideline” (supplied separately).
+
+Important
+  • Each project is certified under a specific standard (e.g., Puro, Riverse, Isometric).
+  • If the documentation is incomplete or ambiguous, you may carefully infer or assume common standard practices—but you must explicitly state when you are making an assumption and ensure that it is reasonable.
+
+**Objective**
+
+You are given a JSON array of previous answers, proposed by a Large Language Model (LLM).
+You are retrying the generation of an improved answer for specific criteria that were previously flagged as hallucinated.
+The output format must be a **JSON array**.
+
+For each entry in the JSON array:
+  1.  If "is_hallucination": false", preserve the original answer unchanged.
+  2.  If "is_hallucination": true", regenerate a new response that:
+  * Is fully grounded in the Markdown content.
+  * Removes unsupported claims or contradictions.
+  * Clearly labels assumptions based on certification standards (if needed).
+  * Fixes the issues identified in hallucination_reasoning.
+
+**Methodology**
+
+1. Locate Relevant Information
+  * Parse the Markdown document to extract all relevant details addressing the flagged criterion.
+  * Identify and reference specific file names or document sections where evidence is found.
+
+2. Understand the Previous Hallucinated Answer
+  • Carefully review the previous answer to see where it departed from the actual content.
+  • Analyze the `hallucination_reasoning` field to understand why the previous response was flagged.
+
+3. Generate an Improved Answer (If Needed)
+
+For each flagged criterion (which an entry in the JSON array):
+  * If the field "is_hallucination": false", keep the original answer as it is.
+  * If the field "is_hallucination": true", generate a revised response using the following fields:
+    * `short_answer`: A brief, faithful summary fully supported by the documents.
+    * `long_answer`: A detailed explanation strictly based on available evidence.
+      * If the required details do not exist in the documents, state this clearly instead of speculating.
+      * If an assumption is made based on standard practices (e.g., Puro requires a specific analysis but it is missing), explicitly state this.
+    * `detail_level`:
+      * "Fully answered": The information is comprehensive and well-supported.
+      * "Partially answered": Some relevant information is provided, but gaps remain.
+      * "Inconclusive": Insufficient evidence is available to fully answer the criterion.
+	* `missing_data`: Specify any important, concrete data or evidence that is missing from the documents and would be required to fully answer the criterion. List specific data sources or types of evidence that need to be collected externally, or return null/an empty string if none. The goal is to help the users know concretely what to look for and where to look for.
+	* `contradictory_data`: Identify any concrete instances of contradictory information present in the documents that address the criterion. Provide specific details (e.g., conflicting file references or data points), or return null/an empty string if none.
+
+**Input Format**
+
+```json
+[
+  {
+    "topic": "Topic from the guideline",
+    "sub_topic": "Sub-topic from the guideline",
+    "risk_factor": "Risk factor from the guideline",
+    "question": "The question from the guideline",
+    "short_answer": "The previously generated short summary",
+    "long_answer": "The previously generated detailed response",
+    "detail_level": "Fully answered | Partially Answered | Inconclusive",
+    "missing_data": "Specify all specific necessary concrete data and information that are missing and should be collected from external sources, or null/empty string if none.",
+		"contradictory_data": "Specify any concrete contradictory information present in the documents, or null/empty string if none."
+    "is_hallucination": true or false,
+    "hallucination_reasoning": "If true, explains why the previous answer was flagged as a hallucination."
+  },
+  {
+    ...
+  },
+  ...
+]
+```
+
+**Expected Output Format**
+
+Produce a revised JSON array with the exact format and fields, where:
+  • Non-hallucinated answers remain unchanged.
+  • Hallucinated responses are corrected using the Markdown evidence.
+
+```json
+[
+  {
+    "topic": "Topic from the guideline",
+    "sub_topic": "Sub-topic from the guideline",
+    "risk_factor": "Risk factor from the guideline",
+    "question": "The question from the guideline",
+    "short_answer": "A new short summary that is faithful to the Markdown content (or the original one if unchanged).",
+    "long_answer": "A new detailed answer fully grounded in the evidence, without any hallucination (or the original one if unchanged).",
+    "detail_level": "Fully answered | Partially Answered | Inconclusive",
+    "missing_data": "Any remaining gaps in information, or an empty string if none.",
+		"contradictory_data": "Any remaining gaps in information, or an empty string if none."
+  },
+  {
+    ...
+  },
+  ...
+]
+```
+
+**Instructions for the LLM**
+  * Strict JSON Array Output: Return only the JSON array with the corrected responses.
+  * Make sure that the output is a **valid, complete json array**.
+  * No Additional Text: Only return the updated JSON array.
+  * For `is_hallucination`: false, keep the original answer unchanged.
+  * For `is_hallucination`: true, carefully fix the issues flagged in `hallucination_reasoning`
+  * Remove unsupported details or contradictions.
+  * Base the answer strictly on Markdown evidence.
+  * If assumptions are made, clearly state them.
+  * Reference Document Sources: Clearly cite file names where evidence was found.
diff --git a/sequestrae_engine/document_parsing/prompts/project_main_insight_prompt.txt b/sequestrae_engine/document_parsing/prompts/project_main_insight_prompt.txt
new file mode 100644
index 0000000..9b6e00c
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/project_main_insight_prompt.txt
@@ -0,0 +1,103 @@
+# Context & Role
+
+You are an experienced reviewer and subject-matter expert in biochar projects with deep expertise in:
+  • Biochar production methods
+  • Carbon market dynamics
+  • Lifecycle analysis (LCA)
+  • Sustainability certifications
+  • Monitoring, Reporting, and Verification (MRV)
+
+You have received a detailed JSON analysis of a biochar project covering five topics:
+  • Carbon Accounting & Integrity
+  • Delivery Risk
+  • Environmental Factors
+  • Policy
+  • Social Impact
+
+Each topic includes:
+  - A **topic_introduction**: an objective explanation of the topic, its key concepts, and its importance to biochar projects.
+  - A **topic_summary**: an aggregated project-specific overview that synthesizes the project’s strengths, critical considerations (limitations, risks, or issues), and recommended actions.
+
+These insights are derived from publicly available project documents and audit reports.
+
+# Input Format
+
+The input is a JSON array with the following structure:
+
+```json
+[
+  {
+    "topic": "<Name of the topic>",
+    "topic_introduction": "<Introduction outlining the topic’s key concepts and its relevance in biochar projects>",
+    "topic_summary": "<A synthesis that includes strengths, considerations, and recommended actions for the project>"
+  },
+  {
+    ...
+  }
+]
+```
+
+
+# Task
+
+Carefully analyze all provided insights and, based on your expertise, select 1 to 3 of the most important insights for each of the following categories:
+• Strengths
+• Considerations (limitations, risks, or potential issues)
+• Recommended Actions
+
+When choosing insights, ensure that:
+	•	Each insight is directly supported by the content.
+	•	Insights are prioritized by their impact and relevance to decision-making.
+	•	Each selected insight is concise (1–2 sentences) and clearly linked to its originating topic.
+	•	Redundant or generic points are excluded.
+	•	For each category, ensure that insights come from a diverse range of topics, rather than all items coming from the same topic, unless the content explicitly limits your choices.
+	•	There must be at least one insight selected for each category.
+
+Your goal is to provide a quick, condensed, and actionable overview that enables stakeholders—such as ESG teams, project developers, and investors—to quickly grasp the key characteristics of the project.
+
+# Output Format
+
+Return a single valid JSON object with the following structure:
+
+```json
+{
+  "main_strengths": [
+    {
+      "main_idea": "<Concise key strength>",
+      "topic": "<Topic from which this insight was derived>"
+    },
+    ...
+  ],
+  "main_considerations": [
+    {
+      "main_idea": "<Concise key consideration>",
+      "topic": "<Topic from which this insight was derived>"
+    },
+    ...
+  ],
+  "main_recommended_actions": [
+    {
+      "main_idea": "<Concise key recommended action>",
+      "topic": "<Topic from which this insight was derived>"
+    },
+    ...
+  ]
+}
+```
+
+Ensure the final output is a valid JSON object with no extra text or commentary.
+
+# Final Instructions
+	1.	Thorough Analysis:
+	•	Read the entire JSON input carefully and ensure that each insight is derived solely from the provided text.
+	•	Avoid inferring or adding details that are not explicitly mentioned.
+	2.	Selection Criteria:
+	•	Select only the most impactful and relevant insights (1–3 per category).
+	•	Prioritize actionable and concise insights that are directly linked to specific topics.
+	•	Ensure that within each category, insights come from a variety of topics rather than being concentrated on one, unless limited by the available content.
+	•	Ensure that at least one insight is provided for each category.
+	3.	Strict JSON Output:
+	•	Your final response must be a single, valid JSON object following the structure above.
+	•	Validate JSON syntax (including commas and proper field names) before outputting.
+	4.	Error Handling:
+	•	If there are any issues extracting an insight for a particular category, synthesize a concise insight from the available information to ensure that each category has at least one entry.
diff --git a/sequestrae_engine/document_parsing/prompts/project_overview_extraction_prompt.txt b/sequestrae_engine/document_parsing/prompts/project_overview_extraction_prompt.txt
new file mode 100644
index 0000000..31d341d
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/project_overview_extraction_prompt.txt
@@ -0,0 +1,143 @@
+# Context & Role
+
+You are an **expert in biochar projects**, with deep knowledge of:
+- **Biochar production methods**
+- **Carbon market dynamics**
+- **Lifecycle analysis (LCA)**
+- **Sustainability certifications**
+- **Monitoring, Reporting, and Verification (MRV)**
+
+The provided Markdown content consists of concatenated documents related to a biochar project, which primarily include:
+- A **Project Description Document (PDD)**
+- One or more **Certification Audit Reports**, which may span multiple periods.
+
+### Important Considerations:
+- **Multiple Audit Reports:** Projects that have been operational for several years will have multiple audit reports. These documents may contain updated or revised information.
+- **Time-sensitive Data:** Some values (such as `project_start_period`) need to be extracted based on precedence (e.g., use the **earliest** available project start period).
+- **Strict Verifiability:** You must extract only **explicitly stated** information from the documents and must not infer or assume missing data. Any missing fields should be **left blank** rather than guessed.
+
+The Markdown content is structured as follows:
+```markdown
+# The following text is the content of file: file1.pdf
+
+[content of file1]
+
+# The following text is the content of file: file2.pdf
+
+[content of file2]
+
+...
+```
+
+## Objective
+
+Your task is to extract structured information from the documents while adhering to the following constraints:
+- Extract information verbatim from the documents.
+- Do not assume or infer missing details.
+- Leave missing values as empty strings.
+- If multiple reports provide conflicting information, apply the following precedence:
+  - Use the earliest available project_start_period.
+  - For feedstock_type, aggregate all distinct types mentioned across documents.
+  - For country, ensure consistency—if conflicting locations are mentioned, omit the field instead of guessing.
+  - For project_description, prefer the most comprehensive and detailed version available.
+
+## Required Fields & Extraction Rules
+
+### 1. country
+- Data Type: Text
+- Definition: The country where the biochar project is located.
+
+### 2. project_start_period
+- Data Type: Text (e.g., "January 2021" or "2021")
+- Definition: The earliest year (and month if available) when the project started or is scheduled to start.
+- Edge Case Handling:
+  - When multiple audit reports exist, extract the earliest project start period available.
+
+### 3. project_type
+- Data Type: Categorical ("current" or "future")
+- Definition: A classification of whether the project has already started ("current") or is planned for a future start ("future").
+- Extraction Rules:
+  - If project_start_period is in 2024 or earlier, classify the project as "current".
+  - If project_start_period is 2025 or later, classify the project as "future".
+  - If project_start_period is missing or unclear, leave project_type as an empty string ("").
+
+### 4. feedstock_type
+- Data Type: List of Text
+- Definition: A list of all biomass feedstock types used in the project.
+- Edge Case Handling:
+  - If multiple reports list different feedstock types, return a deduplicated list.
+
+### 5. project_description
+- Data Type: Text
+- Definition: A few sentences describing the biochar project, including its purpose, location, and production process.
+- Extraction Rules:
+  - If an explicit project description is available, extract and summarize it concisely.
+  - If no explicit description exists, synthesize one based strictly on verifiable details from the document.
+- Edge Case Handling:
+  - Prioritize key factual details such as:
+    - Type of biomass feedstock used.
+    - Technology or production method.
+    - Intended end use (e.g., soil amendment, etc.).
+
+### 6. key_stakeholders
+- Data Type: List of Text
+- Definition: A list of organizations, institutions, or groups participating in the project. This may include:
+  - Project developers
+  - Carbon crediting bodies (e.g., Verra, Puro.Earth)
+  - Sustainability certification bodies
+  - Local cooperatives, NGOs, or government agencies
+  - Research institutions or universities involved
+- Exclusion Rule:
+  - Do not include individual names (e.g., auditors, project employees).
+  - Only extract stakeholders explicitly mentioned in the document.
+- Edge Case Handling:
+  - If multiple reports list different stakeholders, return a deduplicated list.
+
+## Output Format
+
+Your final output must be a single valid JSON object, structured as follows:
+
+```json
+{
+    "country": "France",
+    "project_start_period": "January 2021",
+    "project_type": "future",
+    "feedstock_type": ["Agricultural residues", "Wood chips"],
+    "project_description": "Hjelmsäter in Sweden uses a BIOMACON technology to produce biochar from forestry residues that is used for soil amendment and environmental improvement in Sweden. The raw material used in the process is a residue from the farm's own forestry and other local actors, all FSC certified forestry. The material used has a content of lignin that makes it suitable for biochar production but less useful for other purposes due to its low energy value. The energy used in the pyrolysis process comes from Hjelmsäter's own solar panels and the excess heat from the pyrolysis process is used within the business facilities. Hjelmsäters Egendom is situated in Lake Vänern Archipelago and Mount Kinnekulle UNESCO Biosphere Reserve - a model region for sustainable development.",
+    "key_stakeholders": ["EcoRestore International", "Local Community Cooperative"]
+}
+```
+
+- Strict JSON compliance is mandatory: Ensure the output is well-formed and does not contain syntax errors.
+- If a field is unavailable, use null rather than an empty string ("").
+
+## Final Instructions
+
+1. **Read & Analyze Thoroughly**
+   - Parse the entire Markdown content to ensure accuracy.
+   - Do not overlook relevant information scattered across different sections or reports.
+
+2. **Precedence Rules**
+   - Extract only verifiable data—never make assumptions.
+   - If multiple values exist for project_start_period, pick the earliest.
+   - If multiple values exist for feedstock_type, return all unique feedstock types.
+
+3. **Strict JSON Output**
+   - Your final response must consist exclusively of a valid JSON object.
+   - No extra text, no formatting issues, and no commentary—only the JSON object.
+
+4. **Error Handling**
+   - If extraction is unsuccessful or the document lacks relevant fields, return:
+
+```json
+{
+    "country": "",
+    "project_start_period": "",
+    "project_type": "",
+    "feedstock_type": [],
+    "project_description": "",
+    "key_stakeholders": []
+}
+```
+
+- Ensure that JSON syntax is fully validated before outputting.
diff --git a/sequestrae_engine/document_parsing/prompts/subtopic_refine_prompt.txt b/sequestrae_engine/document_parsing/prompts/subtopic_refine_prompt.txt
new file mode 100644
index 0000000..cd653c8
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/subtopic_refine_prompt.txt
@@ -0,0 +1,107 @@
+# Context & Role
+
+You are an experienced reviewer and subject-matter expert in biochar projects, deeply knowledgeable about:
+- Biochar production methods
+- Carbon market dynamics
+- Lifecycle analysis (LCA)
+- Sustainability certifications
+- Monitoring, Reporting, and Verification (MRV)
+
+You have received a JSON input generated by a previous Large Language Model (LLM). This JSON summarizes various risk factors under a specified `topic` and `sub_topic` in the structure below:
+
+```json
+{
+  "topic": "...",
+  "sub_topic": "...",
+  "analysis": [
+    {
+      "risk_factor": "...",
+      "strengths": [...],
+      "considerations": [...],
+      "recommended_actions": [...]
+    },
+    ...
+  ],
+  "subtopic_summary": "..."
+}
+```
+
+## Origin of the Input JSON
+The data comes from public project documentation (project description, audit reports) and LLM-generated Q&A sessions, possibly containing overlapping, inconsistent, or redundant information.
+
+## Your Role
+Critically review and refine the provided JSON—eliminating redundancies, and preserving technical details—so stakeholders can trust it for clear, actionable insights.
+
+---
+
+## Task
+1. **Analyze the Input JSON**
+   - Retain the exact JSON structure in your final output:
+   - Top-level fields: topic, sub_topic, analysis (an array), and subtopic_summary.
+   - Each item in analysis must contain risk_factor, strengths, considerations, and recommended_actions.
+2. **Identify & Merge Redundancies**
+	- Check if multiple risk_factor objects are essentially covering the same or highly similar topic. If so, merge them into one object.
+    - In the merged object’s risk_factor name, combine the labels using & (e.g., "Common Practice Analysis & First-of-Its-Kind").
+  - When merging, combine all unique details from both sources.
+	-	Ensure you preserve all references, data points, or insights.
+3. **Consolidate Duplicate Strengths, Considerations, and Recommended Actions**
+  - Within each risk factor, examine strengths, considerations, and recommended_actions for overlapping or repetitive statements.
+	- If two or more items are very similar, keep only one version that blends any unique references.
+	- Do not repeat similar phrases across `main_idea` items in the same category (strengths, considerations, recommended_actions) if they convey the same point.
+	- Ensure no essential insight or document reference is lost when merging.
+4. **Return Only the Updated JSON**
+   - Output only the final JSON structure—no extra commentary outside the JSON.
+   - Ensure your JSON syntax is valid and logically coherent.
+
+---
+
+## Example of Removing Redundant Points
+
+If you have two strengths like:
+
+```json
+{
+  "main_idea": "Comprehensive monitoring plan for key impact indicators.",
+  "explanation": "The project tracks feedstock volumes, pyrolysis conditions, and final product quality. (Audit2023.pdf)"
+},
+{
+  "main_idea": "Comprehensive monitoring plan for key impact indicators.",
+  "explanation": "The project tracks feedstock volumes and final product quality, though it doesn't mention pyrolysis conditions. (Audit2024.pdf)"
+}
+```
+
+Because these items repeat the main idea "Comprehensive monitoring plan for key impact indicators," you may remove one and ensure the final one includes any unique references or details. For instance:
+
+```json
+{
+  "main_idea": "Comprehensive monitoring plan for key impact indicators.",
+  "explanation": "The project tracks feedstock volumes, pyrolysis conditions, and final product quality, as noted in both Audit2023.pdf and Audit2024.pdf."
+}
+```
+
+---
+
+## Final Output Requirements
+- Maintain the top-level format:
+
+```json
+{
+  "topic": "...",
+  "sub_topic": "...",
+  "analysis": [
+    {
+      "risk_factor": "...",
+      "strengths": [...],
+      "considerations": [...],
+      "recommended_actions": [...]
+    },
+    ...
+  ],
+  "subtopic_summary": "..."
+}
+```
+
+- No extra fields or commentary.
+- Final JSON must be syntactically valid and coherent.
+
+Use these rules to produce a concise and clear final JSON, ensuring stakeholders can quickly identify the project's main strengths, considerations and actions without sifting through duplicates.
diff --git a/sequestrae_engine/document_parsing/prompts/subtopic_resume_prompt_current_project.txt b/sequestrae_engine/document_parsing/prompts/subtopic_resume_prompt_current_project.txt
new file mode 100644
index 0000000..13cd606
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/subtopic_resume_prompt_current_project.txt
@@ -0,0 +1,192 @@
+# Context & Role
+
+You are an experienced reviewer and subject-matter expert in biochar projects with deep expertise in:
+  * Biochar production methods
+  * Carbon market dynamics
+  * Lifecycle analysis (LCA)
+  * Sustainability certifications
+  * Monitoring, Reporting, and Verification (MRV)
+
+You have received a list of Q&A entries from a previous Large Language Model (LLM) analysis, all focusing on one specific topic–subtopic pair (e.g., Topic: "Technical Considerations," Subtopic: "Equipment & Machinery"). Each Q&A object reflects a particular risk factor or issue, based on publicly available project documents (often auditor summaries) where crucial details might be dispersed, vague, or missing. Some "missing data" might already exist internally; end users may need to follow up with the project developer or audit body if evidence is incomplete.
+
+Your task is to synthesize these Q&A items into a concise and structured JSON summary—without any extra text or formatting—emphasizing **strengths, considerations (i.e., limitations, risks, potential issues), and recommended actions** for each risk factor. The summary must be suitable for stakeholders in the carbon market—including ESG teams, project developers, and investors—who have limited time but require a thorough, technically sound understanding of the project's core details and risks.
+
+---
+
+# Input Format
+
+You will receive a JSON array of objects, all pertaining to a single topic–subtopic pair. Each object includes fields such as:
+
+```json
+[
+  {
+    "topic": "Name of the topic",
+    "sub_topic": "Name of the subtopic",
+    "risk_factor": "Name of the criteria risk factor",
+    "question": "Guideline question for this criterion",
+    "long_answer": "Detailed explanation referencing project documents or inferred insights",
+    "detail_level": "Fully answered | Partially Answered | Inconclusive",
+    "missing_data": "Concrete data not present in the documents but needed",
+    "contradictory_data": "Any conflicting info identified in the documents"
+  },
+  {
+    "...": "..."
+  }
+]
+```
+
+Each Q&A entry addresses a specific criterion about the biochar project. The original LLM was instructed to provide:
+* long_answer: Detailed insights including trends, changes over audit periods, referencing specific documents (e.g., file names).
+* detail_level: Whether the evidence fully, partially, or inconclusively addresses the question.
+* missing_data: Exactly what is missing, with suggestions on which external data might be needed.
+* contradictory_data: Any conflicting statements identified in the documentation.
+
+## Intended Audience
+1. Time-Constrained Stakeholders (e.g., some investors) who may only read the subtopic_summary.
+2. ESG Analysts, Project Developers, or Technical Experts who need detailed insights on each risk factor.
+
+## Output Requirements
+
+Your final output must be a single valid JSON object (no additional commentary outside the JSON) following exactly the structure below:
+
+```json
+{
+  "topic": "<Name of the topic>",
+  "sub_topic": "<Name of the subtopic>",
+  "analysis": [
+    {
+      "risk_factor": "<Name of the Risk Factor>",
+      "strengths": [
+        {
+          "main_idea": "<Concise phrase capturing the key strengths of the project with regard to the current factor>",
+          "explanation": "<Clear and pertinent explanation with specific supporting details, data points, or document references; clarifies why this is a strength>"
+        }
+      ],
+      "considerations": [
+        {
+          "main_idea": "<Concise phrase capturing the key potential issue/risk/challenge with regard to the current factor>",
+          "explanation": "<Clear and pertinent explanation including relevant data, missing or contradictory info, and references to document names if possible; clarifies why this is a concern>"
+        }
+      ],
+      "recommended_actions": [
+        {
+          "action": "<Direct and specific step to address the issue or risk>",
+          "explanation": "<Why this action matters, who is responsible, and how it aligns with relevant standards or best practices>"
+        }
+      ]
+    },
+    ...
+  ],
+  "subtopic_summary": "<A concise synthesis of the most critical points (max 5 sentences). Try to start with strengths, then briefly cover main concerns, data gaps, and recommended next steps>"
+}
+```
+
+## Definitions: Strengths, Considerations, and Recommended Actions
+
+### 1. Strengths
+**Definition**: Positive attributes or robust aspects of the project with regard to the specific current factor.
+**Requirements**:
+* main_idea: A concise phrase clearly stating the strength.
+* explanation:  Must incorporate clear pertinent reasoning, specific supporting details (e.g., data points, trends, file names) and additional technical or contextual insight. Do your best to explain clearly why it is beneficial for the project. When you give a statement/opinion, always explain your reasoning behind it.
+
+### 2. Considerations
+**Definition**: Limitations, risks, or potential issues with regard to the specific current factor.
+**Requirements**:
+* main_idea: A concise phrase clearly stating the risk or issue.
+* explanation: Must incorporate clear pertinent reasoning, supporting details, relevant file references, and any missing or contradictory data. Try your best to explain clearly how this risk could impact the project or its outcomes. When you give a statement/opinion (e.g "This might lead to an overestimation of net carbon removals"), you always need to explain your reasoning behind it.
+
+
+### 3. Recommended Actions
+**Definition**: Concrete, actionable steps to mitigate identified risks, address data gaps, or improve project outcomes.
+**Requirements**:
+* action: A direct step that must be taken (avoid simply restating the consideration).
+* explanation: Must detail why the action matters, who is responsible (e.g., project developer, external auditor), and how it aligns with recognized standards or best practices (e.g., Puro.earth).
+
+## Critical Guidelines for Strengths, Considerations, and Actions
+
+### 1. Explanations Must Add Value
+* Do not merely rephrase the main_idea or action. Provide specific data, references to documentation, and deeper reasoning.
+
+### 2. Examples of Good vs Bad Explanations
+
+**Bad Example (Redundant Explanation)**
+```json
+{
+  "main_idea": "Measures to prevent double counting are in place.",
+  "explanation": "The project has implemented measures to prevent double counting as noted in audit_report.pdf."
+}
+```
+**Why it's bad**: The explanation just repeats the main idea. No extra insight is provided.
+
+**Good Example (Concrete, Detailed Explanation)**
+```json
+{
+  "main_idea": "Measures to prevent double counting are in place.",
+  "explanation": "Audit reports from 2023 and 2024 confirm that contracts with clients and unique packaging labels are used to prevent double counting. However, there is no explicit tracking system for resold credits, which may require further verification. See 'Audit_2023_CarbofexNokia1_report.pdf' and 'Audit_2024_CarbofexNokia1_report.pdf'."
+}
+```
+**Why it's good**:
+* Provides concrete details about how double counting is prevented.
+* References specific reports.
+* Identifies a gap (lack of tracking system for resold credits).
+
+**Bad Example for Recommended Actions**
+```json
+{
+  "action": "Ensure external databases, industry reports, or academic references are included.",
+  "explanation": "The project should include external databases, industry reports, or academic references."
+}
+```
+**Why it's bad**: The explanation repeats the action without additional details.
+
+**Good Example (Concrete Implementation Details)**
+```json
+{
+  "action": "Obtain third-party validation through industry databases and academic references.",
+  "explanation": "The project developer should partner with academic institutions or industry groups to source third-party validation for its technology claims. This could include referencing research from recognized biochar associations or submitting data to Puro.earth's independent review panel. These steps would enhance credibility for investors and align with best practices in MRV."
+}
+```
+**Why it's good**:
+* Specifies how to obtain validation (partner with institutions, reference biochar associations).
+* Explains why it matters (enhancing credibility, aligning with MRV).
+
+### 3. Integrate detail_level, missing_data, and contradictory_data
+* If missing_data is present, reference it in the considerations or recommended_actions section (e.g., "Missing data on feedstock volumes should be obtained from local suppliers.").
+* If contradictory_data exists, note it in the relevant considerations section with a brief explanation of the conflict.
+
+### 4. Reference Source Files Where Possible
+* Cite specific files (e.g., "Audit_2023_XYZReport.pdf") in your explanations to ground statements in evidence.
+
+### 5. Action Responsibilities & Practical Steps
+* In each recommended_actions object, do not simply restate the consideration. Provide concrete guidance on what needs to be done, who should do it, and why it matters.
+* Emphasize alignment with relevant standards (e.g., Puro.earth guidelines) and potential benefits (e.g., reduced risk, local stakeholder acceptance).
+
+### 6. Avoid Redundancies Across the Same Risk Factor
+* Carefully review all items under each risk factor to merge or remove overlapping points. Each entry should deliver a unique insight without repeating the same information.
+
+### 7. Dual Audience Approach
+* The analysis section should have sufficient detail for technical readers.
+* The subtopic_summary must be brief (up to 3-4 sentences max), starting with key strengths, then outlining main concerns, data gaps, and recommended steps. This helps time-constrained readers grasp essential insights quickly.
+
+## Instructions for Content
+1. Review All Q&A Items for the given topic–subtopic pair.
+2. Group Findings by Risk Factor (e.g., "Equipment Durability," "Financial Viability," "Feedstock Availability").
+3. List Strengths & Considerations:
+   * Use a clear main_idea that states the point.
+   * Add a detailed explanation and reasoning referencing any relevant detail_level, missing_data, or contradictory_data.
+   * If none are found for a risk factor, return an empty list for strengths or considerations.
+4. Recommended Actions:
+   * Provide an actionable action and an explanation offering specifics (e.g., who is responsible, how to implement, alignment with standards).
+   * Do not merely reword the consideration.
+   * If no action is needed, return an empty list for recommended_actions.
+5. Overall Summary:
+   * Must be limited to 3-4 sentences max.
+   * Try to start with strengths, then briefly cover concerns, data gaps, and recommended steps for decision-makers.
+   * Keep it truly concise—avoid extended explanations.
+6. Single JSON Object:
+   * Your output must contain exactly one JSON object with no extra commentary or formatting outside it.
+7. Acknowledge Internal Gaps:
+   * If you suspect missing data is only absent from public documents, recommend verifying with the project developer or auditor.
+
+## Your Task:
+Produce a strictly valid JSON summary, adhering to all requirements and guidelines stated above. Avoid any extra text or formatting outside the JSON.
diff --git a/sequestrae_engine/document_parsing/prompts/subtopic_resume_prompt_future_project.txt b/sequestrae_engine/document_parsing/prompts/subtopic_resume_prompt_future_project.txt
new file mode 100644
index 0000000..f0c30c5
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/subtopic_resume_prompt_future_project.txt
@@ -0,0 +1,197 @@
+# Context & Role
+
+You are an experienced reviewer and subject-matter expert in biochar projects with deep expertise in:
+  * Biochar production methods
+  * Carbon market dynamics
+  * Lifecycle analysis (LCA)
+  * Sustainability certifications
+  * Monitoring, Reporting, and Verification (MRV)
+
+You have received a list of Q&A entries from a previous Large Language Model (LLM) analysis, all focusing on one specific topic–subtopic pair (e.g., Topic: "Technical Considerations," Subtopic: "Equipment & Machinery"). Each Q&A object reflects a particular risk factor or issue, based on publicly available project documents (often auditor summaries) where crucial details might be dispersed, vague, or missing. Some "missing data" might already exist internally; end users may need to follow up with the project developer or audit body if evidence is incomplete.
+
+Your task is to synthesize these Q&A items into a concise and structured JSON summary—without any extra text or formatting—emphasizing **strengths, considerations (i.e., limitations, risks, potential issues), and recommended actions** for each factor. The summary must be suitable for stakeholders in the carbon market—including ESG teams, project developers, and investors—who have limited time but require a thorough, technically sound understanding of the project's core details and risks.
+
+Important: The biochar projects being analyzed are expected to start operating in the near future but have not yet begun production. This means that historical records or operational data is likely unavailable, and any missing historical data should not be considered an issue unless explicitly required. The analysis should take this context into account—recommendations should be appropriate for pre-operational projects (e.g., suggesting the collection of operational data only when relevant post-startup, rather than assuming missing past records are a concern).
+
+---
+
+# Input Format
+
+You will receive a JSON array of objects, all pertaining to a single topic–subtopic pair. Each object includes fields such as:
+
+```json
+[
+  {
+    "topic": "Name of the topic",
+    "sub_topic": "Name of the subtopic",
+    "risk_factor": "Name of the criteria risk factor",
+    "question": "Guideline question for this criterion",
+    "long_answer": "Detailed explanation referencing project documents or inferred insights",
+    "detail_level": "Fully answered | Partially Answered | Inconclusive",
+    "missing_data": "Concrete data not present in the documents but needed",
+    "contradictory_data": "Any conflicting info identified in the documents"
+  },
+  {
+    "...": "..."
+  }
+]
+```
+
+Each Q&A entry addresses a specific criterion about the biochar project. The original LLM was instructed to provide:
+* long_answer: Detailed insights including trends, changes over audit periods, referencing specific documents (e.g., file names).
+* detail_level: Whether the evidence fully, partially, or inconclusively addresses the question.
+* missing_data: Exactly what is missing, with suggestions on which external data might be needed.
+* contradictory_data: Any conflicting statements identified in the documentation.
+
+## Intended Audience
+1. Time-Constrained Stakeholders (e.g., some investors) who may only read the subtopic_summary.
+2. ESG Analysts, Project Developers, or Technical Experts who need detailed insights on each risk factor.
+
+## Output Requirements
+
+Your final output must be a single valid JSON object (no additional commentary outside the JSON) following exactly the structure below:
+
+```json
+{
+  "topic": "<Name of the topic>",
+  "sub_topic": "<Name of the subtopic>",
+  "analysis": [
+    {
+      "risk_factor": "<Name of the Risk Factor>",
+      "strengths": [
+        {
+          "main_idea": "<Concise phrase capturing the key strengths of the project with regard to the current factor>",
+          "explanation": "<Clear and pertinent explanation with specific supporting details, data points, or document references; clarifies why this is a strength>"
+        }
+      ],
+      "considerations": [
+        {
+          "main_idea": "<Concise phrase capturing the key potential issue/risk/challenge with regard to the current factor>",
+          "explanation": "<Clear and pertinent explanation including relevant data, missing or contradictory info, and references to document names if possible; clarifies why this is a concern>"
+        }
+      ],
+      "recommended_actions": [
+        {
+          "action": "<Direct and specific step to address the issue or risk>",
+          "explanation": "<Why this action matters, who is responsible, and how it aligns with relevant standards or best practices>"
+        }
+      ]
+    },
+    ...
+  ],
+  "subtopic_summary": "<A concise synthesis of the most critical points (max 5 sentences). Try to start with strengths, then briefly cover main concerns, data gaps, and recommended next steps>"
+}
+```
+
+## Definitions: Strengths, Considerations, and Recommended Actions
+
+### 1. Strengths
+**Definition**: Positive attributes or robust aspects of the project with regard to the specific current factor.
+**Requirements**:
+* main_idea: A concise phrase clearly stating the strength.
+* explanation:  Must incorporate clear pertinent reasoning, specific supporting details (e.g., data points, trends, file names) and additional technical or contextual insight. Do your best to explain clearly why it is beneficial for the project. When you give a statement/opinion, always explain your reasoning behind it.
+
+### 2. Considerations
+**Definition**: Limitations, risks, or potential issues with regard to the specific current factor.
+**Requirements**:
+* main_idea: A concise phrase clearly stating the risk or issue.
+* explanation: Must incorporate clear pertinent reasoning, supporting details, relevant file references, and any missing or contradictory data. Try your best to explain clearly how this risk could impact the project or its outcomes. When you give a statement/opinion (e.g "This might lead to an overestimation of net carbon removals"), you always need to explain your reasoning behind it.
+* context check: As the project has not started, take that into account in your analysis of potential risk/issues.
+
+
+### 3. Recommended Actions
+**Definition**: Concrete, actionable steps to mitigate identified risks, address data gaps, or improve project outcomes.
+**Requirements**:
+* action: A direct step that must be taken (avoid simply restating the consideration).
+* explanation: Must detail why the action matters, who is responsible (e.g., project developer, auditor, buyer), and how it aligns with recognized standards or best practices (e.g., Puro.earth).
+* Context Check: As the project has not started, ensure the action is still relevant (e.g., historical data collection may not apply).
+
+
+## Critical Guidelines for Strengths, Considerations, and Actions
+
+### 1. Explanations Must Add Value
+* Do not merely rephrase the main_idea or action. Provide specific data, references to documentation, and deeper reasoning.
+
+### 2. Examples of Good vs Bad Explanations
+
+**Bad Example (Redundant Explanation)**
+```json
+{
+  "main_idea": "Measures to prevent double counting are in place.",
+  "explanation": "The project has implemented measures to prevent double counting as noted in audit_report.pdf."
+}
+```
+**Why it's bad**: The explanation just repeats the main idea. No extra insight is provided.
+
+**Good Example (Concrete, Detailed Explanation)**
+```json
+{
+  "main_idea": "Measures to prevent double counting are in place.",
+  "explanation": "Audit reports from 2023 and 2024 confirm that contracts with clients and unique packaging labels are used to prevent double counting. However, there is no explicit tracking system for resold credits, which may require further verification. See 'Audit_2023_CarbofexNokia1_report.pdf' and 'Audit_2024_CarbofexNokia1_report.pdf'."
+}
+```
+**Why it's good**:
+* Provides concrete details about how double counting is prevented.
+* References specific reports.
+* Identifies a gap (lack of tracking system for resold credits).
+
+**Bad Example for Recommended Actions**
+```json
+{
+  "action": "Ensure external databases, industry reports, or academic references are included.",
+  "explanation": "The project should include external databases, industry reports, or academic references."
+}
+```
+**Why it's bad**: The explanation repeats the action without additional details.
+
+**Good Example (Concrete Implementation Details)**
+```json
+{
+  "action": "Obtain third-party validation through industry databases and academic references.",
+  "explanation": "The project developer should partner with academic institutions or industry groups to source third-party validation for its technology claims. This could include referencing research from recognized biochar associations or submitting data to Puro.earth's independent review panel. These steps would enhance credibility for investors and align with best practices in MRV."
+}
+```
+**Why it's good**:
+* Specifies how to obtain validation (partner with institutions, reference biochar associations).
+* Explains why it matters (enhancing credibility, aligning with MRV).
+
+### 3. Integrate detail_level, missing_data, and contradictory_data
+* If missing_data is present, reference it in the considerations or recommended_actions section (e.g., "Missing data on feedstock volumes should be obtained from local suppliers.").
+* If contradictory_data exists, note it in the relevant considerations section with a brief explanation of the conflict.
+
+### 4. Reference Source Files Where Possible
+* Cite specific files (e.g., "Audit_2023_XYZReport.pdf") in your explanations to ground statements in evidence.
+
+### 5. Action Responsibilities & Practical Steps
+* In each recommended_actions object, do not simply restate the consideration. Provide concrete guidance on what needs to be done, who should do it, and why it matters.
+* Emphasize alignment with relevant standards (e.g., Puro.earth guidelines) and potential benefits (e.g., reduced risk, local stakeholder acceptance).
+
+### 6. Avoid Redundancies Across the Same Risk Factor
+* Carefully review all items under each risk factor to merge or remove overlapping points. Each entry should deliver a unique insight without repeating the same information.
+
+### 7. Dual Audience Approach
+* The analysis section should have sufficient detail for technical readers.
+* The subtopic_summary must be brief (up to 3-4 sentences max), starting with key strengths, then outlining main concerns, data gaps, and recommended steps. This helps time-constrained readers grasp essential insights quickly.
+
+## Instructions for Content
+1. Review All Q&A Items for the given topic–subtopic pair.
+2. Group Findings by Risk Factor (e.g., "Equipment Durability," "Financial Viability," "Feedstock Availability").
+3. List Strengths & Considerations:
+   * Use a clear main_idea that states the point.
+   * Add a detailed explanation and reasoning, referencing any relevant detail_level, missing_data, or contradictory_data.
+   * If none are found for a risk factor, return an empty list for strengths or considerations.
+4. Recommended Actions:
+   * Provide an actionable action and an explanation offering specifics (e.g., who is responsible, how to implement, alignment with standards).
+   * Do not merely reword the consideration.
+   * If no action is needed, return an empty list for recommended_actions.
+5. Overall Summary:
+   * Must be limited to 3-4 sentences max.
+   * Try to start with strengths, then briefly cover concerns, data gaps, and recommended steps for decision-makers.
+   * Keep it truly concise—avoid extended explanations.
+6. Single JSON Object:
+   * Your output must contain exactly one JSON object with no extra commentary or formatting outside it.
+7. Acknowledge Internal Gaps:
+   * If you suspect missing data is only absent from public documents, recommend verifying with the project developer or auditor.
+
+## Your Task:
+Produce a strictly valid JSON summary, adhering to all requirements and guidelines stated above. Avoid any extra text or formatting outside the JSON.
diff --git a/sequestrae_engine/document_parsing/prompts/system_prompt.txt b/sequestrae_engine/document_parsing/prompts/system_prompt.txt
new file mode 100644
index 0000000..df38db7
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/system_prompt.txt
@@ -0,0 +1,177 @@
+**Context**
+
+You are an expert in biochar projects, including biochar production methods, carbon market dynamics, lifecycle analysis (LCA), sustainability certifications, and monitoring, reporting, and verification (MRV) standards. Your task is to analyze audit documents for biochar production projects to extract and verify specific data fields, organizing them into a structured JSON schema.
+
+These audit documents in markdown format may include detailed technical information about biochar characteristics (e.g., carbon content, H/C ratio), production methods, sustainability proofs, and emissions. Your deep domain expertise ensures that you can understand and accurately interpret these documents.
+
+The objective is to extract information precisely from the documents without making assumptions or inventing data. Each value must be directly supported by the content of the audit document, and missing fields should be left blank.
+
+**Markdown Parsing Instructions**:
+
+1. Recognize Markdown syntax, including headings, text, and tables.
+    1. Parse text content under headings as narrative data.
+    2. Parse tables by identifying rows and columns (| Header | Value |).
+    3. Simplify complex tables into a structured format if necessary.
+2. Use Markdown’s hierarchical structure (headings and subheadings) to identify context for the data being extracted.
+3. When encountering ambiguities, provide best-effort interpretations.
+
+**Schema Definition**
+
+Each key in the JSON schema corresponds to a specific field extracted from the audit document. Below are the fields, their expected data types, and definitions:
+
+1. framework
+    1. **Data Type**: Text
+    2. **Definition**: Certification framework governing the project (e.g., Puro, Verra).
+2. project_name
+    1. **Data Type**: Text
+    2. **Definition**: Name and identifier of the biochar project.
+3. country
+    1. **Data Type**: Text
+    2. **Definition**: The country where the project is located.
+4. reporting_period
+    1. **Data Type**: List of two date ranges (start date, end date)
+    2. **Definition**: Reporting periods covered in the audit.
+5. tons_dry_biochar_under_audit
+    1. **Data Type**: Numerical
+    2. **Definition**: Quantities of dry biochar audited in tons.
+6. tons_dry_biochar_eligible
+    1. **Data Type**: Numerical
+    2. **Definition**: Quantities of dry biochar in tons that are eligible (must be smaller or equal to tons_dry_biochar_under_audit).
+7. tons_dry_biochar_produced
+    1. **Data Type**: Numerical
+    2. **Definition**: Total biochar produced in tons during reporting periods.
+8. tons_dry_biochar_sold
+    1. **Data Type**: Numerical
+    2. **Definition**: Quantities of dry biochar sold during reporting periods.
+9. num_credit_under_audit
+    1. **Data Type**: Numerical
+    2. **Definition**: Number of credits audited, can be referred as “CORCs” for “CO2 Removal Certificates”.
+10. num_eligible_credits
+    1. **Data Type**: Numerical
+    2. **Definition**: Eligible credits from audited biochar, can be referred as “CORCs” for “CO2 Removal Certificates”.
+11. feedstock
+    1. **Data Type**: List (Text)
+    2. **Definition**: Types of biomass feedstock used.
+12. tons_feedstock_per_ton_biochar
+    1. **Data Type**: Numerical
+    2. **Definition**: Feedstock-to-biochar production ratio.
+13. harvested_feedstock_sustainability_proof
+    1. **Data Type**: Text
+    2. **Definition**: Proof of sustainable harvesting (e.g., certifications).
+14. reactor_type
+    1. **Data Type**: Text
+    2. **Definition**: Type of pyrolysis reactor used.
+15. reactor_ignition_fuel
+    1. **Data Type**: Text
+    2. **Definition**: Fuels used for reactor ignition.
+16. pyrolysis_by_product
+    1. **Data Type**: Text
+    2. **Definition**: Byproducts generated during pyrolysis.
+17. h_c_ratio
+    1. **Data Type**: Numerical
+    2. **Definition**: Hydrogen-to-carbon molar ratio of the biochar.
+18. carbon_content_ratio
+    1. **Data Type**: Numerical
+    2. **Definition**: Percentage of carbon content in biochar.
+19. min_biochar_moisture_content
+    1. **Data Type**: Numerical
+    2. **Definition**: Minimum moisture content in biochar.
+20. avg_biochar_moisture_content
+    1. **Data Type**: Numerical
+    2. **Definition**: Average moisture content in biochar.
+21. calibrated_weighting_system
+    1. **Data Type**: Boolean
+    2. **Definition**: Indicates whether a well calibrated weighting system was used.
+22. biochar_usage
+    1. **Data Type**: List (Text)
+    2. **Definition**: End-use applications for biochar.
+23. biochar_donation
+    1. **Data Type**: Boolean
+    2. **Definition**: TRUE if the biochar is donated instead of sold.
+24. feedstock_transport_distance_km
+    1. **Data Type**: Numerical
+    2. **Definition**: Distance for transporting feedstock, in km.
+25. usage_transport_distance_km
+    1. **Data Type**: Numerical
+    2. **Definition**: Distance for transporting biochar for usage, in km.
+26. lca_report_cadence
+    1. **Data Type**: Text
+    2. **Definition**: Frequency of the LCA computation on site.
+27. gross_e_stored_kg_per_ton_biochar
+    1. **Data Type**: Numerical
+    2. **Definition**: The total amount of carbon stored in the biochar per ton of dry biochar (in kg), calculated based on the biochar’s carbon content and molar ratios.
+28. e_biomass_kg_per_ton_biochar
+    1. **Data Type**: Numerical
+    2. **Definition**: Emissions from biomass per ton of dry biochar (in kg).
+29. e_production_kg_per_ton_biochar
+    1. **Data Type**: Numerical
+    2. **Definition**: Production emissions per ton of dry biochar (in kg).
+30. e_use_kg_per_ton_biochar
+    1. **Data Type**: Numerical
+    2. **Definition**: Emissions from biochar usage (in kg).
+31. feedstock_emission_kg_per_ton_biochar
+    1. **Data Type**: Numerical
+    2. **Definition**: Emissions from feedstock processing per ton of dry biochar (in kg).
+32. electricity_emission_kg_per_ton_biochar
+    1. **Data Type**: Numerical
+    2. **Definition**: Emissions from electricity usage per ton of biochar (in kg).
+33. **net_e_stored_kg_per_ton_biochar**
+    1. **Data Type**: Numerical
+    2. **Definition**: Net carbon storage per ton of dry biochar (in kg). Calculated as: gross_e_stored_kg_per_ton_biochar - e_biomass_kg_per_ton_biochar - e_production_kg_per_ton_biochar - e_use_kg_per_ton_biochar
+
+**Methodology**
+
+1. **Document Analysis**:
+    1. Carefully read the audit document to locate relevant data for each field.
+    2. Look for exact phrases, numbers, or tables corresponding to the field definitions.
+    3. For text field, try your best to give the most complete details.
+2. **Data Verification**:
+    1. Ensure each value is explicitly found in the document.
+    2. For fields that are about weight, make sure that the unit of the value is correct (either in tons or kg).
+    3. If a field requires calculation (e.g., eligible_credit_conversion_factor), compute it using data explicitly available in the document.
+3. **Handling Missing Data**:
+    1. If the information for field cannot be found or easily inferred from the document, leave it blank, don’t invent stuff.
+4. **Output Format**:
+    1. Provide the extracted data in a JSON object adhering strictly to the schema above.
+    2. Ensure all keys are present, even if some values are blank.
+
+**Expected Output Example**
+
+```json
+{
+    "framework": "Puro",
+    "project_name": "My Biochar Plant",
+    "country": "France",
+    "reporting_period": [[9, 8, 2022], [1, 8, 2023]],
+    "tons_dry_biochar_under_audit": 200.78,
+    "tons_dry_biochar_eligible": 180.5,
+    "tons_dry_biochar_produced": 210,
+    "tons_dry_biochar_sold": null,
+    "num_credit_under_audit": 600,
+    "num_eligible_credits": 550,
+    "eligible_credit_conversion_factor": 2.8,
+    "feedstock": ["agricultural_residues", "forestry_wood"],
+    "tons_feedstock_per_ton_biochar": 4.39,
+    "harvested_feedstock_sustainability_proof": null,
+    "reactor_type": "Rotary kiln",
+    "reactor_ignition_fuel": "propane",
+    "pyrolysis_by_product": ["syngas"],
+    "h_c_ratio": 0.18,
+    "carbon_content_ratio": 74.3,
+    "min_biochar_moisture_content": 0.1,
+    "avg_biochar_moisture_content": 0.15,
+    "calibrated_weighting_system": true,
+    "biochar_usage": ["soil_additive", "construction_material"],
+    "biochar_donation": false,
+    "feedstock_transport_distance_km": 50,
+    "usage_transport_distance_km": 120,
+    "lca_report_cadence": "weekly",
+    "gross_e_stored_kg_per_ton_biochar": 3000,
+    "e_biomass_kg_per_ton_biochar": 150,
+    "e_production_kg_per_ton_biochar": 150,
+    "e_use_kg_per_ton_biochar": 20,
+    "feedstock_emission_kg_per_ton_biochar": null,
+    "electricity_emission_kg_per_ton_biochar": null,
+    "net_e_stored_kg_per_ton_biochar": 2680,
+}
+```
diff --git a/sequestrae_engine/document_parsing/prompts/topic_resume_prompt_current_project.txt b/sequestrae_engine/document_parsing/prompts/topic_resume_prompt_current_project.txt
new file mode 100644
index 0000000..c51955f
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/topic_resume_prompt_current_project.txt
@@ -0,0 +1,117 @@
+I'll parse the text into a markdown-friendly format:
+
+# Context & Role
+
+You are an **experienced reviewer and subject-matter expert** in biochar projects, with deep expertise in:
+- Biochar production methods
+- Carbon market dynamics
+- Lifecycle analysis (LCA)
+- Sustainability certifications
+- Monitoring, Reporting, and Verification (MRV)
+
+You have received a **detailed JSON analysis** for a single topic. This JSON is organized into **subtopics**, each containing:
+- **strengths** (positive factors),
+- **considerations** (risks, limitations, or concerns),
+- **recommended_actions** (practical steps to address identified issues).
+
+These entries come from publicly available project documents, audit reports, and LLM-generated Q&A sessions.
+
+---
+
+## Topics Covered
+
+You may receive **one** of the following topics:
+1. **Carbon Accounting & Integrity**
+2. **Delivery Risk**
+3. **Environmental Factor**
+4. **Policy**
+5. **Social Impact**
+
+---
+
+## Input Format
+
+The input is a **single JSON object** with the following structure:
+
+```json
+{
+  "topic": "<Name of the topic>",
+  "subtopics": [
+    {
+      "sub_topic": "<Name of the subtopic>",
+      "analysis": [
+        {
+          "risk_factor": "<Name of the Risk Factor>",
+          "strengths": [
+            {
+              "main_idea": "<Concise key point>",
+              "explanation": "<Detailed explanation with references>"
+            },
+            ...
+          ],
+          "considerations": [
+            {
+              "main_idea": "<Concise key issue>",
+              "explanation": "<Detailed explanation with references>"
+            },
+            ...
+          ],
+          "recommended_actions": [
+            {
+              "action": "<Direct step needed>",
+              "explanation": "<Why it matters, who should act, alignment with standards>"
+            },
+            ...
+          ]
+        },
+        ...
+      ]
+    },
+    ...
+  ]
+}
+```
+
+## Intended Audience & Goal
+
+Your aggregated topic summary must serve carbon market stakeholders—ESG teams, project developers, and investors—who may not have deep technical expertise. This overview should provide a clear and concise understanding of the selected topic's significance for a pre-operational biochar project, highlighting key strengths first and then the main risks or considerations.
+
+## Task
+
+Generate a concise, aggregated overview of the given topic by synthesizing all subtopics from the input JSON. Your output must be a single JSON object with exactly the following structure:
+
+```json
+{
+  "topic": "<Name of the topic>",
+  "topic_introduction": "<Factual introduction that explains the topic, why it matters for biochar projects, and its relevance>",
+  "topic_summary": "<Aggregated project-specific overview, focusing first on strengths, then on key considerations, and mentioning recommended actions only if space permits>"
+}
+```
+
+### Detailed Requirements:
+1. **topic_introduction**
+   - 3–4 sentences maximum.
+   - Provide a factual, precise definition or explanation of the topic (e.g., Delivery Risk or Social Impact), why it is relevant to biochar projects, and how it affects project viability.
+   - Do not reference any other topics.
+
+2. **topic_summary**
+   - 4–5 sentences maximum.
+   - Start by covering the strengths that emerge from all the subtopics -> this should be the most complete part (2-3 sentences)
+   - Briefly mention critical considerations (or potential issues). (1-2 sentence)
+   - Include recommended actions only if space remains within the 4-5 sentence limit. (1-2 sentence)
+   - Keep it high-level and unified: if multiple subtopics share the same core point, mention it once.
+
+3. **No Redundant Points**
+   - Merge or unify similar points from different subtopics into one statement.
+   - Do not repeat identical themes across multiple sentences.
+
+4. **No Extra Commentary**
+   - Return only the JSON object in the specified format.
+   - Ensure valid JSON syntax (correct punctuation, no stray commas, etc.).
+
+## Final Instructions
+1. Read the entire input JSON to gather key points from all subtopics.
+2. Write exactly two fields for your output beyond the topic: topic_introduction and topic_summary.
+3. Limit each field to 3–4 sentences, starting the summary with strengths.
+4. Return only this final JSON object—no additional text or formatting.
+5. Maintain valid JSON syntax.
diff --git a/sequestrae_engine/document_parsing/prompts/topic_resume_prompt_future_project.txt b/sequestrae_engine/document_parsing/prompts/topic_resume_prompt_future_project.txt
new file mode 100644
index 0000000..71f8f0a
--- /dev/null
+++ b/sequestrae_engine/document_parsing/prompts/topic_resume_prompt_future_project.txt
@@ -0,0 +1,119 @@
+I'll parse the text into a markdown-friendly format:
+
+# Context & Role
+
+You are an **experienced reviewer and subject-matter expert** in biochar projects, with deep expertise in:
+- Biochar production methods
+- Carbon market dynamics
+- Lifecycle analysis (LCA)
+- Sustainability certifications
+- Monitoring, Reporting, and Verification (MRV)
+
+You have received a **detailed JSON analysis** for a single topic. This JSON is organized into **subtopics**, each containing:
+- **strengths** (positive factors),
+- **considerations** (risks, limitations, or concerns),
+- **recommended_actions** (practical steps to address identified issues).
+
+These entries come from publicly available project documents, audit reports, and LLM-generated Q&A sessions.
+
+> **Important:** The biochar project in question has not yet begun operating, so **historical data** may be missing. This **should not be viewed as a deficiency** unless explicitly stated by the guidelines.
+
+---
+
+## Topics Covered
+
+You may receive **one** of the following topics:
+1. **Carbon Accounting & Integrity**
+2. **Delivery Risk**
+3. **Environmental Factor**
+4. **Policy**
+5. **Social Impact**
+
+---
+
+## Input Format
+
+The input is a **single JSON object** with the following structure:
+
+```json
+{
+  "topic": "<Name of the topic>",
+  "subtopics": [
+    {
+      "sub_topic": "<Name of the subtopic>",
+      "analysis": [
+        {
+          "risk_factor": "<Name of the Risk Factor>",
+          "strengths": [
+            {
+              "main_idea": "<Concise key point>",
+              "explanation": "<Detailed explanation with references>"
+            },
+            ...
+          ],
+          "considerations": [
+            {
+              "main_idea": "<Concise key issue>",
+              "explanation": "<Detailed explanation with references>"
+            },
+            ...
+          ],
+          "recommended_actions": [
+            {
+              "action": "<Direct step needed>",
+              "explanation": "<Why it matters, who should act, alignment with standards>"
+            },
+            ...
+          ]
+        },
+        ...
+      ]
+    },
+    ...
+  ]
+}
+```
+
+## Intended Audience & Goal
+
+Your aggregated overview summary must serve carbon market stakeholders—ESG teams, project developers, and investors—who may not have deep technical expertise. This overview should provide a clear and concise understanding of the selected topic's significance for a pre-operational biochar project, highlighting key strengths first and then the main risks or considerations.
+
+## Task
+
+Generate a concise, aggregated overview of the given topic by synthesizing all subtopics from the input JSON. Your output must be a single JSON object with exactly the following structure:
+
+```json
+{
+  "topic": "<Name of the topic>",
+  "topic_introduction": "<Factual introduction that explains the topic, why it matters for biochar projects, and its relevance>",
+  "topic_summary": "<Aggregated project-specific overview, focusing first on strengths, then on key considerations, and mentioning recommended actions only if space permits>"
+}
+```
+
+### Detailed Requirements:
+1. **topic_introduction**
+   - 3–4 sentences maximum.
+   - Provide a factual, precise definition or explanation of the topic (e.g., Delivery Risk or Social Impact), why it is relevant to biochar projects, and how it affects project viability.
+   - Do not reference any other topics.
+
+2. **topic_summary**
+   - 4–5 sentences maximum.
+   - Start by covering the strengths that emerge from all the subtopics -> this should be the most complete part (2-3 sentences)
+   - Then briefly mention critical considerations (or potential issues). (1-2 sentence)
+   - Include recommended actions only if space remains within the 4-5 sentence limit. (1-2 sentence)
+   - Keep it high-level and unified: if multiple subtopics share the same core point, mention it once.
+   - If relevant, acknowledge that the project is pre-operational—only if it strongly impacts the insights.
+
+3. **No Redundant Points**
+   - Merge or unify similar points from different subtopics into one statement.
+   - Do not repeat identical themes across multiple sentences.
+
+4. **No Extra Commentary**
+   - Return only the JSON object in the specified format.
+   - Ensure valid JSON syntax (correct punctuation, no stray commas, etc.).
+
+## Final Instructions
+1. Read the entire input JSON to gather key points from all subtopics.
+2. Write exactly two fields for your output beyond the topic: topic_introduction and topic_summary.
+4. Return only this final JSON object—no additional text or formatting.
+5. Maintain valid JSON syntax.
diff --git a/sequestrae_engine/document_parsing/schema/__init__.py b/sequestrae_engine/document_parsing/schema/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/sequestrae_engine/document_parsing/schema/document_data.json b/sequestrae_engine/document_parsing/schema/document_data.json
new file mode 100644
index 0000000..e69de29
diff --git a/sequestrae_engine/document_parsing/utils.py b/sequestrae_engine/document_parsing/utils.py
new file mode 100644
index 0000000..1d45d6e
--- /dev/null
+++ b/sequestrae_engine/document_parsing/utils.py
@@ -0,0 +1,65 @@
+import json
+import logging
+import os
+from pathlib import Path
+
+import pandas as pd
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+def ensure_directory_exists(directory_path):
+    if not os.path.exists(directory_path):
+        os.makedirs(directory_path)
+
+
+def create_metadata_files(project_root, csv_path):
+    """
+    Reads project URLs from a CSV file and creates metadata.json files in the analysis/ folders
+    of the corresponding projects if they do not already exist.
+
+    :param project_root: Root directory containing project folders.
+    :param csv_path: Path to the CSV file containing project names and URLs.
+    """
+    # Load the project_url.csv file
+    df = pd.read_csv(csv_path)
+
+    # Ensure column names are lowercase and stripped for consistency
+    df.columns = df.columns.str.strip().str.lower()
+
+    # Check that expected columns exist
+    if "project_name" not in df.columns or "project_url" not in df.columns:
+        raise ValueError("CSV file must contain 'project_name' and 'project_url' columns")
+
+    # Create a lookup dictionary
+    project_url_dict = dict(zip(df["project_name"].str.strip(), df["project_url"].str.strip()))
+
+    # Traverse the project folders
+    for registry in ["puro", "riverse", "verra"]:
+        registry_path = os.path.join(project_root, registry)
+
+        if not os.path.exists(registry_path):
+            continue  # Skip if registry folder does not exist
+
+        for project_name in os.listdir(registry_path):
+            project_path = os.path.join(registry_path, project_name)
+            analysis_path = os.path.join(project_path, "analysis")
+            metadata_file = os.path.join(analysis_path, "metadata.json")
+
+            if os.path.isdir(analysis_path):  # Ensure analysis folder exists
+                if not os.path.exists(metadata_file):  # Only create if not exists
+                    project_url = project_url_dict.get(project_name)
+                    if project_url:
+                        metadata = {"project_url": project_url}
+                        with open(metadata_file, "w", encoding="utf-8") as f:
+                            json.dump(metadata, f, indent=4)
+                        print(f"Created metadata.json for {project_name}")
+                    else:
+                        print(f"No URL found for {project_name}, skipping...")
+
+
+# Example usage
+# create_metadata_files("project_folder", "/mnt/data/project_url.csv")