Epstein Files Database
A searchable database and network analysis tool for the DOJ Epstein Files release. Built to make public records accessible, cross-referenced, and analyzable.
What This Does
- Entity Extraction — Extracts names, organizations, locations, and dates from 4,055 DOJ documents
- Relationship Mapping — Builds a graph of connections based on document co-occurrence
- Layer Classification — Classifies entities by degree of separation from Jeffrey Epstein
- Cross-Reference Engine — Fuzzy-matches entities against:
- PPP loan data (SBA)
- FEC campaign contributions
- Federal grant recipients
- Pattern Detection Agent — AI agent specialized in finding non-obvious connections
Architecture
┌─────────────────────────────────────────────────────────────────┐
│ Frontend (React + Tailwind) │
│ • Search Interface • Network Visualization • Document Viewer │
└─────────────────────────┬───────────────────────────────────────┘
│
┌─────────────────────────▼───────────────────────────────────────┐
│ API Server (Go) │
│ • REST Endpoints • Full-text Search • Graph Queries │
└─────────────────────────┬───────────────────────────────────────┘
│
┌─────────────────────────▼───────────────────────────────────────┐
│ Data Layer │
│ ┌──────────────┐ ┌──────────────┐ ┌────────────────────────┐ │
│ │ PostgreSQL │ │ Neo4j │ │ Typesense/Meilisearch │ │
│ │ Entities │ │ Graph │ │ Full-text Search │ │
│ │ Documents │ │ Relations │ │ │ │
│ │ Cross-refs │ │ │ │ │ │
│ └──────────────┘ └──────────────┘ └────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────▼───────────────────────────────────────┐
│ Extraction Pipeline (TypeScript) │
│ • OCR Processing • NER Extraction • Relationship Inference │
└─────────────────────────────────────────────────────────────────┘
Tech Stack
| Component | Technology | Rationale |
|---|---|---|
| Frontend | React + Tailwind + Vite | Fast, modern, type-safe |
| API | Go (Fiber/Echo) | Performance for graph queries |
| Primary DB | PostgreSQL | Structured data, JSONB, full-text |
| Graph DB | Neo4j | Relationship traversal at scale |
| Search | Typesense | Fast fuzzy search, typo-tolerant |
| Extraction | TypeScript + LLM | Entity extraction, deduplication |
| Pattern Agent | OpenClaw sub-agent | AI-driven connection discovery |
Data Sources
Primary: DOJ Epstein Files
- 4,055 documents (EFTA00000001 through EFTA00008528)
- 1.77M lines of OCR text
- 157GB raw data (PDFs, images, scans)
- Source: https://www.justice.gov/epstein
Cross-Reference Datasets
- PPP Loans: SBA FOIA data (https://data.sba.gov/dataset/ppp-foia)
- FEC Contributions: Federal Election Commission (https://www.fec.gov/data/)
- Federal Grants: USASpending.gov (https://www.usaspending.gov/download_center/custom_award_data)
Layer Classification
| Layer | Definition | Example |
|---|---|---|
| L0 | Jeffrey Epstein himself | — |
| L1 | Direct associates (named in documents with Epstein) | Ghislaine Maxwell |
| L2 | One degree removed (connected to L1 but not directly to Epstein) | — |
| L3 | Two degrees removed | — |
Getting Started
Prerequisites
- Docker & Docker Compose
- Node.js 20+
- Go 1.21+
- PostgreSQL 16+ (or use Docker)
- Neo4j 5+ (or use Docker)
Quick Start
# Clone the repo
git clone https://github.com/subculture-collective/epstein-db.git
cd epstein-db
# Start databases
docker-compose up -d
# Install dependencies
npm install
cd api && go mod download && cd ..
# Run extraction pipeline (requires OpenAI-compatible API)
cp .env.example .env
# Edit .env with your API keys
npm run extract
# Start the API server
cd api && go run . &
# Start the frontend
npm run dev
Project Structure
epstein-db/
├── api/ # Go API server
│ ├── cmd/ # Entry points
│ ├── internal/ # Internal packages
│ │ ├── handlers/ # HTTP handlers
│ │ ├── db/ # Database access
│ │ ├── graph/ # Neo4j operations
│ │ └── search/ # Typesense operations
│ └── pkg/ # Public packages
│
├── extraction/ # TypeScript extraction pipeline
│ ├── src/
│ │ ├── ocr/ # OCR processing
│ │ ├── ner/ # Named Entity Recognition
│ │ ├── dedup/ # Entity deduplication
│ │ └── cross-ref/ # Cross-reference matching
│ └── scripts/ # Pipeline scripts
│
├── frontend/ # React frontend
│ ├── src/
│ │ ├── components/ # UI components
│ │ ├── pages/ # Route pages
│ │ ├── hooks/ # Custom hooks
│ │ └── api/ # API client
│ └── public/
│
├── agents/ # AI agents
│ └── pattern-finder/ # Connection discovery agent
│
├── data/ # Data directory (gitignored)
│ ├── raw/ # Symlink to DataSources
│ ├── processed/ # Extracted entities/relations
│ ├── crossref/ # PPP, FEC, grants data
│ └── exports/ # Generated exports
│
├── docker-compose.yml # Database services
├── schema/ # Database schemas
│ ├── postgres/ # SQL migrations
│ └── neo4j/ # Cypher constraints
│
└── docs/ # Documentation
├── ARCHITECTURE.md
├── DATA_MODEL.md
└── CONTRIBUTING.md
Roadmap
Phase 1: Foundation ✅
- Repository setup
- Database schema design
- Docker compose for databases
- Basic extraction pipeline
Phase 2: Entity Extraction
- OCR text ingestion
- Named Entity Recognition (NER)
- Entity deduplication (LLM-assisted)
- Document-entity relationships
Phase 3: Graph Construction
- Neo4j schema
- Co-occurrence relationship building
- Layer classification algorithm
- Graph API endpoints
Phase 4: Cross-Reference
- PPP loan data ingestion
- FEC contribution data ingestion
- Federal grants data ingestion
- Fuzzy matching engine
Phase 5: Frontend
- Search interface
- Network visualization (D3/Force-Graph)
- Document viewer
- Entity detail pages
Phase 6: Pattern Agent
- Agent architecture design
- Connection hypothesis generation
- Validation pipeline
- Report generation
Contributing
This is an open research project. Contributions welcome:
- Entity extraction improvements
- Fuzzy matching algorithms
- UI/UX improvements
- Additional cross-reference datasets
- Pattern detection strategies
License
MIT License. The code is open source. The documents are public records.
Disclaimer
This is an independent research project. We make no representations about the completeness or accuracy of the analysis. This tool surfaces connections — it does not assert guilt, criminality, or wrongdoing.