Dome Document Intelligence

Extracts structured data from any business document — with per-field confidence scores, governance validation, and a full audit trail — without storing the document.

Covers the Orchestrate & Model phases of the DOME method. Part of the Dome portfolio. For cross-cutting Dome context see dome-docs.

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What it does

Upload a PDF, image, or spreadsheet and the tool:

1. Auto-detects the document type (invoice, purchase order, trade confirmation, contract, etc.) — no templates or configuration needed
2. Extracts all significant fields with per-field confidence scores
3. Validates the extraction against a configurable set of governance rules
4. Logs an audit event (metadata and hash only — document content is never stored)
5. Flags whether human review is required

The key differentiator within the Dome portfolio: this tool is designed to run with a locally deployed AI (Ollama), meaning no document content ever leaves the client's infrastructure. Cloud (Claude API) and Azure OpenAI modes are also supported via a provider switch.

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Status

Layer	Status
Backend (FastAPI)	Complete
Frontend (Next.js)	Complete
Claude provider	Working
Ollama provider	Stubbed — in progress
Azure OpenAI provider	Stubbed

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Evaluation

Extraction quality and the governance rules engine are measured by an auditable, four-layer evaluation harness in backend/eval/ — see EVAL.md for the full methodology and a sample report.

• Rules engine — all 16 governance rules are unit-tested (CI-gated, deterministic).
• Extraction — scored against a committed golden set with type-aware normalization: field precision/recall/F1, value-match, doc-type accuracy, end-to-end human-in-loop agreement.
• Confidence calibration — a reliability table + ECE that checks the model's stated confidence against its actual accuracy.
• Validated LLM judge — checked against ground truth before it is trusted for fuzzy fields; each live run logs one governance eval_judgment event.

cd backend
make eval         # live: re-run the model + validated judge → eval_report.{json,md}
make eval-score   # offline: score the committed recordings (no network)

CI runs the deterministic layers against recorded outputs and never calls a live model.

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Quick start

Requirements

• Python 3.12
• Node.js 20+
• Ollama (optional — for local AI mode)

Setup

# Clone
git clone https://github.com/Dome-Layer/dome-document-intelligence.git
cd dome-document-intelligence

# Backend
cd backend
python -m venv venv
source venv/bin/activate      # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Environment
cp .env.example .env
# Edit .env with your values — see Environment variables below

Run locally

# Backend (from backend/)
uvicorn app.main:app --reload --port 8000

# Frontend (from frontend/)
npm install
npm run dev

Backend API: http://localhost:8000. Health check: GET /api/health. Frontend: http://localhost:3000.

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Environment variables

Create backend/.env from .env.example. Required variables:

# LLM — choose one mode
LLM_PROVIDER=claude              # claude | azure_openai | ollama

# Claude (cloud demo)
ANTHROPIC_API_KEY=sk-ant-...
CLAUDE_MODEL=claude-sonnet-4-6   # optional — override the Claude model

# Azure OpenAI (client tenant — not yet implemented)
AZURE_OPENAI_ENDPOINT=
AZURE_OPENAI_KEY=
AZURE_OPENAI_DEPLOYMENT=

# Ollama (local / air-gapped — not yet implemented)
OLLAMA_URL=http://localhost:11434

# Supabase
SUPABASE_URL=
SUPABASE_ANON_KEY=
SUPABASE_SERVICE_ROLE_KEY=

# App
ENVIRONMENT=development          # development | production
CORS_ORIGINS=http://localhost:3000
MAX_FILE_SIZE_MB=20

Never commit real secrets. backend/.env is gitignored; .env.example is not.

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API endpoints

Method	Path	Description
POST	/api/extract	Upload a document and run extraction
GET	/api/rules	List governance rules for the authenticated user
PATCH	/api/rules/{rule_id}	Toggle a rule enabled/disabled
GET	/api/audit	Paginated governance event log
GET	/api/audit/{event_id}	Single audit event detail
GET	/api/health	Health check

All endpoints except /api/health require a Supabase bearer token. Authentication uses Supabase magic link, triggered from the DOME website login page.

Example extraction call

curl -X POST http://localhost:8000/api/extract \
  -H "Authorization: Bearer <supabase-token>" \
  -F "[email protected]"

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Database setup

Run the canonical DOME governance schema — dome-docs/templates/governance_schema.sql — in your Supabase SQL editor to create the required tables (governance_rules, governance_events) and indexes. For in-place upgrades to an existing deployment, apply the numbered files in dome-docs/migrations/. Governance rules for each user are seeded automatically on first GET /api/rules.

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Deployment

Cloud demo

• Backend: Railway (Dockerfile deploy, EU region)
• Frontend: Vercel (EU region) — not yet deployed

Client Azure tenant

Azure OpenAI provider not yet implemented. See TOOL_CONTEXT.md for planned architecture.

Air-gapped / local AI

Ollama provider not yet implemented. Target models: Llama 3.2 Vision, Qwen2-VL. See TOOL_CONTEXT.md for planned architecture.

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Repository structure

dome-document-intelligence/
├── README.md                   This file
├── EVAL.md                     Evaluation methodology + sample report
├── LICENSE
├── SECURITY.md
├── backend/                    FastAPI service
│   ├── Makefile                eval / eval-score / test / lint targets
│   ├── eval/                   Evaluation harness (golden set, scorer, calibration, judge)
│   ├── app/
│   │   ├── main.py             App entry point, lifespan, CORS
│   │   ├── api/                Route handlers (extract, rules, audit)
│   │   ├── core/               Config, DB client, logging
│   │   ├── models/             Pydantic schemas
│   │   ├── providers/          LLM provider implementations
│   │   └── services/           Ingest, extraction, validation, governance
│   ├── supabase_schema.sql     Pointer stub — canonical schema lives in dome-docs/templates/
│   ├── Dockerfile
│   ├── railway.toml
│   └── requirements.txt
└── frontend/                   Next.js application
    ├── app/                    Pages (upload, result, rules, audit)
    ├── components/             UI components (upload, result, layout, auth)
    ├── context/                Auth context
    ├── lib/                    API client, auth helpers, types
    └── styles/

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Related

• dome-docs — cross-cutting Dome documentation
• Portfolio status