Your Documents as a
Knowledge Graph
wtrdb transforms company documents into a living knowledge graph that AI agents can reason over through a single URL.
Vector search finds paragraphs.
wtrdb connects facts.
Traditional RAG returns chunks of text. Knowledge graphs understand relationships between entities across your entire document corpus.
Three steps to knowledge intelligence
Extraction
Upload documents. Our AI extracts entities, facts, and relationships from unstructured text.
Adjudication
Review and refine extracted knowledge. Merge duplicates, resolve conflicts, add context.
Query
Your AI agents query the knowledge graph through a single URL. Instant, grounded answers.
Full control over your knowledge pipeline
Multi-Format Ingestion
PDF, DOCX, HTML, Markdown, and more. Upload any document format seamlessly.
Grounded Extraction
Extract entities and relationships with citations back to source documents.
Cost & Time Preview
Know exactly what extraction will cost and how long it will take before you start.
Model-Per-Stage Control
Choose different LLMs for extraction, adjudication, and query stages.
Live Progress
Watch extraction happen in real-time with detailed progress updates.
Graph Visualization
Explore your knowledge graph visually with interactive node exploration.
MCP Brain Endpoints
Expose your knowledge graph as Model Context Protocol endpoints.
Per-Endpoint API Keys
Fine-grained access control with unique API keys per integration.
Why Graph beats Vector
Deploy anywhere
Ubuntu / Debian Package
One-line install with apt
Self-Host Ready
Run on your own infrastructure
Systemd Service
Production-ready daemon management
Enterprise Deployment
Air-gapped and compliance-ready
Turn your company knowledge into an AI brain
Join the organizations already powering their AI agents with wtrdb knowledge graphs.