FinTree
Canonical GAAP P&L Ontology for Humans and AI Agents

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A structured, machine-readable P&L (income statement) hierarchy tree covering 234 US GAAP line items. Every node includes XBRL tags, ASC references, variance driver playbooks, real-company comparability examples, and chart-of-accounts mappings.

Built for FP&A teams, AI financial agents, and accounting software integrations.

Features

• 234 P&L nodes organized in a hierarchical tree from Net Income down to granular line items
• XBRL mapped to real US GAAP taxonomy tags verified against SEC EDGAR
• 5 industry overlays (SaaS, Manufacturing, Retail, Financial Services, Professional Services)
• Variance driver playbooks with specific increase/decrease root causes per line item
• Real company comparability examples (Apple, Salesforce, Amazon, McDonald's, etc.)
• Chart of Accounts mappings for QuickBooks, NetSuite, and SAP
• AI context tags for semantic search and agent consumption
• Interactive web explorer with responsive mobile design
• RESTful API for programmatic access

Quick Start

# Clone
git clone https://github.com/gtm-k/fintree.git
cd fintree

# Install
pip install -e packages/core
pip install -e packages/api

# Run
cd packages/api
uvicorn fintree_api.main:app --port 8000

# Open http://localhost:8000

API

Endpoint	Description
GET /api/tree/stats	Tree statistics (node counts, levels)
GET /api/tree/pl-sections	P&L sections in financial statement order
GET /api/tree/full	Complete tree with all nodes and edges
GET /api/nodes/detail?id=fintree:NetIncome	Full node detail (26 fields)
GET /api/nodes/ancestors?id=...	Breadcrumb ancestor chain
GET /api/nodes/children?id=...	Direct children of a node
GET /api/search?q=revenue	Fuzzy search across all nodes
GET /api/industry	Available industry overlays
GET /api/tree/pl-sections?industry=saas	P&L with SaaS overlay applied

Tree Structure

Net Income
├── Pre-Tax Income (EBT)
│   ├── Operating Income (EBIT)
│   │   ├── Gross Profit
│   │   │   ├── Net Revenue (26 nodes)
│   │   │   └── Cost of Revenue (66 nodes)
│   │   └── Operating Expenses (91 nodes)
│   │       ├── Selling Expenses
│   │       ├── G&A Expenses
│   │       ├── R&D Expenses
│   │       └── Depreciation & Amortization
│   └── Non-Operating Income & Expenses (16 nodes)
├── Income Tax Expense (6 nodes)
└── Below-the-Line Items (5 nodes)

Node Data

Each of the 234 nodes includes:

Field	Example
id	fintree:ProductRevenue
xbrl_tag	us-gaap:RevenueFromContractWithCustomerExcludingAssessedTax
definition	Revenue from the sale of physical goods
formula_human	Finished Goods + Component Sales + Licensing
asc_reference	ASC 606
normal_balance	CREDIT
variance_drivers	Volume, pricing, mix, seasonal drivers with playbooks
comparability	Apple: "Products (iPhone, Mac, iPad, Wearables)"
coa_mapping	QB: 4100, NetSuite: 4100, SAP: 8100000
ai_context_tags	[revenue, product, asc_606, top_line]

Project Structure

packages/
├── core/          # TreeGraph library (pip installable)
├── api/           # FastAPI server
├── data/
│   ├── nodes/     # 234 YAML node definitions
│   ├── industry/  # 5 industry overlay configs
│   ├── non-gaap/  # Non-GAAP measure definitions
│   ├── tree.json  # Compiled tree (generated)
│   └── scripts/   # Compiler and validator
└── web/
    └── static/    # Interactive frontend (HTML/CSS/JS)

Industry Overlays

Apply an overlay to emphasize relevant nodes and suppress irrelevant ones:

Overlay	Emphasized	Suppressed
SaaS	Subscription Revenue, Cloud Hosting, R&D	Direct Materials, Manufacturing Overhead
Manufacturing	Direct Labor, Raw Materials, Factory Costs	Cloud Compute, Subscription Revenue
Retail	Product Revenue, Inventory, Shipping	R&D, Cloud Infrastructure
Financial Services	Interest Income/Expense, Trading	Manufacturing, Inventory
Professional Services	Billable Labor, Utilization	Manufacturing, Inventory

License

Apache 2.0