ARCnet's core architecture—data integration, predictive modeling, risk surfacing, and agentic decision support—is domain-agnostic. This document details how the platform adapts to specific industries through configuration rather than code changes.
Each industry deployment requires three configuration layers:
┌─────────────────────────────────────────────────────────────────┐
│ Domain Configuration │
│ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │
│ │ Schema Mapping│ │ Agent Personas│ │ Risk Weights │ │
│ │ (data model) │ │ (specialists) │ │ (priorities) │ │
│ └───────────────┘ └───────────────┘ └───────────────┘ │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Core Platform │
│ ETL Engine │ ML Models │ Agent Orchestrator │ NLP │ Vision │
└─────────────────────────────────────────────────────────────────┘
A manufacturing plant needs to optimize production uptime while managing maintenance costs and production schedules.
| Source Type | Examples | Ingestion Method |
|---|---|---|
| Equipment sensors | SCADA, IoT telemetry | API/streaming |
| Maintenance systems | CMMS exports, work orders | Excel/CSV |
| Production schedules | MES, ERP exports | Excel/API |
| Financial data | Cost center reports, budgets | Excel/CSV |
| Quality data | Inspection logs, defect reports | CSV/photos |
# configs/manufacturing.yaml
dimensions:
org_unit:
source_column: "cost_center"
display_name: "Production Line"
asset:
source_column: "equipment_id"
attributes:
- asset_class: "machine_type"
- location: "cell_location"
- criticality: "production_impact"
event:
source_column: "production_order"
attributes:
- event_type: "order_type" # Production Run, Changeover, Maintenance Window
- start_date: "scheduled_start"
- end_date: "scheduled_end"
facts:
budget_execution:
table: "fact_cost_tracking"
measures:
- budgeted: "planned_cost"
- actual: "actual_cost"
- variance: "cost_variance"
maintenance:
table: "fact_work_orders"
measures:
- labor_hours: "maintenance_hours"
- parts_cost: "materials_cost"
- downtime: "equipment_downtime_hrs"
production:
table: "fact_production"
measures:
- units_produced: "good_units"
- scrap: "rejected_units"
- cycle_time: "actual_cycle_time"# configs/manufacturing_agents.yaml
specialists:
- id: "production_planner"
name: "Production Planning Specialist"
domain: "production"
data_access:
- fact_production
- dim_event
prompt_context: |
You are a production planning expert. Analyze production schedules,
identify bottlenecks, and assess capacity constraints. Consider
changeover times, batch sizes, and demand variability.
- id: "maintenance_engineer"
name: "Maintenance Engineering Specialist"
domain: "maintenance"
data_access:
- fact_work_orders
- dim_asset
- ml_predictions.equipment_failure
prompt_context: |
You are a reliability engineer. Analyze equipment health, maintenance
history, and failure predictions. Prioritize maintenance activities
based on production impact and cost.
- id: "plant_controller"
name: "Plant Controller"
domain: "finance"
data_access:
- fact_cost_tracking
- dim_org_unit
prompt_context: |
You are the plant financial controller. Monitor cost performance,
identify variances, and forecast spending. Balance maintenance
investment against production value.# configs/manufacturing_ml.yaml
predictive_maintenance:
target: "days_to_failure"
features:
- operating_hours_since_last_service
- vibration_trend_7d
- temperature_anomaly_score
- age_months
- failure_history_count
model: "xgboost_survival"
prediction_horizons: [7, 14, 30, 90]
production_risk:
target: "schedule_slip_probability"
features:
- equipment_availability_forecast
- material_availability
- labor_availability
- historical_changeover_variance
model: "gradient_boosted_classifier"- "Which production lines are at risk for next week's orders?"
- "What is the projected maintenance spend for Q4?"
- "Which machines should we service during the holiday shutdown?"
- "Show me the top 5 equipment reliability risks"
A hospital system needs to optimize OR scheduling, manage medical equipment availability, and forecast departmental budgets.
| Source Type | Examples | Ingestion Method |
|---|---|---|
| Clinical systems | EHR exports, OR schedules | API/HL7 |
| Equipment management | Biomed work orders, PM schedules | Excel/CSV |
| Financial systems | Department budgets, charge data | Excel/CSV |
| Staffing | Scheduling systems, timekeeping | API/CSV |
| Supply chain | Inventory levels, purchase orders | Excel/API |
# configs/healthcare.yaml
dimensions:
org_unit:
source_column: "department_code"
display_name: "Department"
hierarchy:
- facility
- service_line
- department
asset:
source_column: "equipment_id"
attributes:
- asset_class: "device_type" # MRI, Ventilator, Infusion Pump
- location: "unit_location"
- criticality: "clinical_impact"
- fda_class: "device_class"
event:
source_column: "case_id"
attributes:
- event_type: "procedure_type"
- start_date: "scheduled_datetime"
- duration: "estimated_duration"
- surgeon: "primary_surgeon"
- or_room: "operating_room"
facts:
budget_execution:
table: "fact_department_financials"
measures:
- budgeted: "approved_budget"
- actual: "ytd_expenses"
- revenue: "ytd_revenue"
maintenance:
table: "fact_biomed_work_orders"
measures:
- labor_hours: "tech_hours"
- parts_cost: "parts_expense"
- downtime: "equipment_oos_hours"
cases:
table: "fact_surgical_cases"
measures:
- case_count: "completed_cases"
- turnover_time: "room_turnover_minutes"
- cancellation_rate: "cancelled_cases"# configs/healthcare_agents.yaml
specialists:
- id: "or_coordinator"
name: "OR Scheduling Coordinator"
domain: "operations"
data_access:
- fact_surgical_cases
- dim_event
- dim_asset
prompt_context: |
You are an OR scheduling expert. Analyze surgical schedules,
equipment requirements, and room availability. Identify scheduling
conflicts and optimization opportunities. Consider surgeon preferences,
case complexity, and turnover requirements.
- id: "biomed_manager"
name: "Biomedical Engineering Manager"
domain: "maintenance"
data_access:
- fact_biomed_work_orders
- dim_asset
- ml_predictions.equipment_failure
prompt_context: |
You are the biomedical engineering manager. Analyze medical equipment
reliability, PM compliance, and failure predictions. Prioritize based
on patient safety impact and regulatory requirements.
- id: "department_administrator"
name: "Department Administrator"
domain: "finance"
data_access:
- fact_department_financials
- dim_org_unit
prompt_context: |
You are a healthcare financial administrator. Monitor department
budgets, analyze expense trends, and forecast financial performance.
Consider reimbursement patterns and volume trends.
- id: "nursing_director"
name: "Nursing Director"
domain: "personnel"
data_access:
- fact_staffing
- dim_org_unit
prompt_context: |
You are the nursing director. Analyze staffing levels, skill mix,
and scheduling patterns. Identify coverage gaps and overtime trends.# configs/healthcare_ml.yaml
equipment_availability:
target: "available_for_scheduled_case"
features:
- pm_compliance_score
- days_since_last_failure
- device_age_years
- utilization_rate_30d
- pending_repair_orders
model: "xgboost_classifier"
case_cancellation_risk:
target: "will_cancel"
features:
- patient_comorbidity_score
- equipment_availability_forecast
- surgeon_historical_cancel_rate
- days_until_procedure
- pre_op_clearance_status
model: "gradient_boosted_classifier"
budget_forecast:
target: "monthly_expense"
features:
- case_volume_forecast
- seasonal_adjustment
- supply_cost_trend
- labor_cost_trend
model: "sarimax"- "Which surgeries this week are at risk due to equipment availability?"
- "What is the projected ICU budget variance for the quarter?"
- "Which devices should be prioritized for preventive maintenance?"
- "Show me OR utilization trends and cancellation patterns"
A general contractor managing multiple construction projects needs to predict equipment needs, track project costs, and optimize resource allocation across job sites.
| Source Type | Examples | Ingestion Method |
|---|---|---|
| Project management | Primavera, MS Project exports | Excel/CSV |
| Equipment tracking | GPS/telematics, rental invoices | API/CSV |
| Financial systems | Job cost reports, change orders | Excel/CSV |
| Subcontractor data | Invoices, schedules | Excel/CSV |
| Site documentation | Progress photos, inspection reports | Photos/PDF |
# configs/construction.yaml
dimensions:
org_unit:
source_column: "job_number"
display_name: "Project"
hierarchy:
- region
- project
- phase
asset:
source_column: "equipment_id"
attributes:
- asset_class: "equipment_type" # Crane, Excavator, Loader
- ownership: "owned_or_rented"
- location: "current_jobsite"
event:
source_column: "activity_id"
attributes:
- event_type: "activity_type"
- start_date: "planned_start"
- end_date: "planned_finish"
- critical_path: "is_critical"
facts:
budget_execution:
table: "fact_job_cost"
measures:
- budgeted: "original_budget"
- committed: "committed_cost"
- actual: "actual_cost"
- forecast: "estimated_at_completion"
equipment:
table: "fact_equipment_usage"
measures:
- hours: "operating_hours"
- fuel: "fuel_consumed"
- idle_time: "idle_hours"
- utilization: "utilization_rate"
schedule:
table: "fact_schedule_progress"
measures:
- planned_pct: "baseline_percent_complete"
- actual_pct: "actual_percent_complete"
- variance_days: "schedule_variance"# configs/construction_agents.yaml
specialists:
- id: "project_manager"
name: "Project Manager"
domain: "operations"
data_access:
- fact_schedule_progress
- dim_event
- ml_predictions.schedule_risk
prompt_context: |
You are a senior project manager. Analyze schedule performance,
identify critical path risks, and assess resource constraints.
Consider weather impacts, subcontractor dependencies, and
inspection requirements.
- id: "equipment_manager"
name: "Equipment Manager"
domain: "logistics"
data_access:
- fact_equipment_usage
- dim_asset
- ml_predictions.equipment_failure
prompt_context: |
You are the equipment fleet manager. Optimize equipment allocation
across job sites, predict maintenance needs, and manage rental
decisions. Consider mobilization costs and utilization rates.
- id: "project_accountant"
name: "Project Accountant"
domain: "finance"
data_access:
- fact_job_cost
- dim_org_unit
prompt_context: |
You are a construction project accountant. Monitor job costs,
analyze change order impacts, and forecast final project cost.
Identify cost overruns early and recommend corrective actions.# configs/construction_vision.yaml
site_progress:
model: "construction_progress_cnn"
inputs:
- drone_imagery
- progress_photos
outputs:
- percent_complete_estimate
- activity_detection
- safety_compliance_flags
document_extraction:
model: "construction_ocr"
document_types:
- daily_reports
- inspection_forms
- delivery_tickets- "Which projects are at risk of missing their milestone?"
- "Where should we reallocate the tower crane next month?"
- "What is our projected cost overrun across all active jobs?"
- "Show me equipment utilization by job site"
A delivery company needs to predict vehicle maintenance needs, optimize routes, and manage fleet costs across multiple distribution centers.
| Source Type | Examples | Ingestion Method |
|---|---|---|
| Telematics | GPS, engine diagnostics | API/streaming |
| Maintenance | Shop work orders, parts inventory | Excel/CSV |
| Operations | Route plans, delivery logs | API/CSV |
| Financial | Fuel costs, lease payments | Excel/CSV |
| Driver data | HOS logs, inspection reports | API/photos |
# configs/fleet.yaml
dimensions:
org_unit:
source_column: "location_code"
display_name: "Distribution Center"
asset:
source_column: "vehicle_id"
attributes:
- asset_class: "vehicle_type" # Tractor, Trailer, Van
- make_model: "vehicle_make_model"
- model_year: "year"
- mileage: "current_odometer"
event:
source_column: "route_id"
attributes:
- event_type: "route_type"
- date: "dispatch_date"
- stops: "stop_count"
facts:
maintenance:
table: "fact_shop_work_orders"
measures:
- labor_hours: "mechanic_hours"
- parts_cost: "parts_total"
- downtime: "vehicle_oos_days"
operations:
table: "fact_route_performance"
measures:
- miles: "total_miles"
- fuel: "fuel_gallons"
- stops: "completed_stops"
- on_time_pct: "on_time_delivery_rate"
costs:
table: "fact_vehicle_costs"
measures:
- fuel_cost: "fuel_expense"
- maintenance_cost: "maintenance_expense"
- lease_cost: "lease_payment"# configs/fleet_agents.yaml
specialists:
- id: "dispatch_supervisor"
name: "Dispatch Operations Supervisor"
domain: "operations"
data_access:
- fact_route_performance
- dim_event
- ml_predictions.vehicle_availability
prompt_context: |
You are the dispatch operations supervisor. Optimize route
assignments, manage driver schedules, and ensure delivery
commitments are met. Consider HOS regulations, customer
time windows, and vehicle capabilities.
- id: "fleet_maintenance"
name: "Fleet Maintenance Manager"
domain: "maintenance"
data_access:
- fact_shop_work_orders
- dim_asset
- ml_predictions.breakdown_risk
prompt_context: |
You are the fleet maintenance manager. Predict vehicle failures,
schedule preventive maintenance, and manage parts inventory.
Minimize unplanned breakdowns while controlling maintenance costs.
- id: "fleet_controller"
name: "Fleet Financial Controller"
domain: "finance"
data_access:
- fact_vehicle_costs
- dim_org_unit
prompt_context: |
You are the fleet financial controller. Analyze total cost of
ownership, compare owned vs leased economics, and optimize
fleet size. Monitor fuel efficiency and identify cost reduction
opportunities.# configs/fleet_ml.yaml
breakdown_prediction:
target: "breakdown_within_7_days"
features:
- miles_since_last_service
- engine_fault_code_count
- oil_life_remaining
- brake_wear_indicator
- tire_pressure_variance
- ambient_temperature_exposure
model: "xgboost_classifier"
route_optimization:
objective: "minimize_total_cost"
constraints:
- hos_compliance
- time_windows
- vehicle_capacity
model: "rl_routing_agent"# configs/fleet_vision.yaml
damage_assessment:
model: "vehicle_damage_classifier"
inputs:
- driver_submitted_photos
- dock_camera_footage
outputs:
- damage_detected: boolean
- severity: [minor, moderate, severe]
- damage_type: [dent, scratch, crack, structural]
- estimated_repair_cost: float- "Which vehicles should we service this weekend to avoid Monday breakdowns?"
- "What is our projected fuel cost variance for the quarter?"
- "Show me breakdown trends by vehicle age and mileage"
- "Which distribution center has the highest maintenance cost per mile?"
An electric utility needs to predict equipment failures, optimize maintenance crew scheduling, and manage capital project portfolios.
| Source Type | Examples | Ingestion Method |
|---|---|---|
| SCADA/DMS | Real-time grid data | API/streaming |
| Asset management | Equipment records, inspection history | Excel/CSV |
| Outage management | OMS data, trouble reports | API/CSV |
| Financial | Capital budgets, O&M costs | Excel/CSV |
| Inspection | Drone imagery, thermography | Photos |
# configs/utility.yaml
dimensions:
org_unit:
source_column: "district_code"
display_name: "Service District"
hierarchy:
- region
- district
- substation
asset:
source_column: "equipment_id"
attributes:
- asset_class: "equipment_type" # Transformer, Breaker, Line
- voltage_class: "voltage_level"
- install_date: "in_service_date"
- condition: "asset_health_index"
event:
source_column: "work_order_id"
attributes:
- event_type: "work_type" # PM, Corrective, Capital
- priority: "work_priority"
- scheduled_date: "planned_date"
facts:
reliability:
table: "fact_outage_events"
measures:
- customer_interruptions: "customers_affected"
- duration_minutes: "outage_duration"
- saidi_contribution: "saidi_minutes"
- saifi_contribution: "saifi_count"
maintenance:
table: "fact_work_orders"
measures:
- labor_hours: "crew_hours"
- material_cost: "materials_expense"
- contractor_cost: "contractor_expense"
capital:
table: "fact_capital_projects"
measures:
- budget: "approved_budget"
- spent: "actual_spend"
- forecast: "forecast_at_completion"# configs/utility_agents.yaml
specialists:
- id: "grid_operations"
name: "Grid Operations Engineer"
domain: "operations"
data_access:
- fact_outage_events
- dim_asset
- ml_predictions.failure_probability
prompt_context: |
You are a grid operations engineer. Analyze equipment health,
predict failures, and assess reliability impacts. Consider
load patterns, weather exposure, and system configuration.
- id: "maintenance_planner"
name: "Maintenance Planning Supervisor"
domain: "maintenance"
data_access:
- fact_work_orders
- dim_event
- ml_predictions.equipment_condition
prompt_context: |
You are the maintenance planning supervisor. Optimize crew
scheduling, prioritize work orders, and manage contractor
resources. Balance preventive and corrective maintenance
to maximize reliability within budget.
- id: "capital_manager"
name: "Capital Program Manager"
domain: "finance"
data_access:
- fact_capital_projects
- dim_org_unit
prompt_context: |
You are the capital program manager. Track project execution,
forecast spending, and prioritize investments based on risk
and reliability impact. Consider regulatory requirements
and rate case implications.# configs/utility_vision.yaml
aerial_inspection:
model: "powerline_defect_detector"
inputs:
- drone_imagery
- helicopter_patrol_video
outputs:
- defects_detected: list
- defect_severity: [low, medium, high, critical]
- component_affected: string
- gps_location: coordinates
thermal_analysis:
model: "thermal_anomaly_detector"
inputs:
- infrared_images
outputs:
- hotspots_detected: list
- temperature_delta: float
- failure_risk_score: float# configs/utility_rl.yaml
crew_scheduling:
environment: "crew_dispatch_env"
state_space:
- pending_work_orders
- crew_locations
- travel_times
- work_priorities
action_space: "discrete_assignment"
reward_function:
- minimize_travel_time: 0.3
- maximize_priority_completion: 0.4
- balance_crew_workload: 0.2
- minimize_overtime: 0.1
maintenance_prioritization:
environment: "asset_maintenance_env"
state_space:
- equipment_health_indices
- failure_probabilities
- budget_remaining
- reliability_metrics
action_space: "ranking"
reward_function:
- maximize_reliability: 0.5
- minimize_cost: 0.3
- regulatory_compliance: 0.2- "Which transformers are at highest risk of failure this summer?"
- "What is our projected SAIDI impact from deferred maintenance?"
- "Optimize crew assignments for tomorrow's planned work"
- "Show me capital project spending variance by district"
To deploy ARCnet in a new industry:
Map your data sources to the canonical dimensional model:
- Identify organizational hierarchy (dim_org_unit)
- Define asset taxonomy (dim_asset)
- Characterize events/activities (dim_event)
- Specify fact tables and measures
Define specialist agents for your domain:
- Identify key decision-making roles
- Specify data access permissions
- Write domain-specific prompt context
- Configure autonomy levels
Configure prediction targets and features:
- Define prediction objectives (classification, regression, time-to-event)
- Identify available features from your data
- Select appropriate model architectures
- Configure prediction horizons and thresholds
- Identify visual data sources (documents, photos, video)
- Select or train appropriate models
- Configure extraction pipelines
Configure how different factors contribute to overall risk:
- Operational impact weights
- Financial impact weights
- Regulatory/compliance weights
- Safety/criticality weights
configs/
├── {industry}/
│ ├── schema.yaml # Data model mapping
│ ├── agents.yaml # Agent personas and prompts
│ ├── ml.yaml # ML model configurations
│ ├── vision.yaml # Vision module settings
│ ├── rl.yaml # RL optimization settings
│ ├── risk_weights.yaml # Risk scoring configuration
│ └── etl_mappings/ # File-type specific column mappings
│ ├── budget_report.yaml
│ ├── maintenance_log.yaml
│ └── schedule_export.yaml
The platform loads configuration at startup, enabling the same codebase to serve multiple industries without modification.