You give it documents. It finds the ideas, connects them, and remembers what contradicts what.
Feed the system your documents - research papers, notes, articles, reports. It reads them and extracts the key ideas. Then it connects those ideas: this concept supports that one, this claim contradicts that one, this cause leads to that effect.
Unlike a search engine that just finds keywords, this system understands meaning. Ask "what causes inflation?" and it finds concepts related to inflation's causes - even if those exact words don't appear in your documents.
Unlike a chatbot that makes things up, every idea traces back to its source. You can always ask "where did this come from?" and get a real answer.
This isn't just a search tool for humans. It's infrastructure for AI that can reason about what it knows.
Most AI "memory" is just similarity search - find things that look like what you asked for. This system tracks:
- Grounding: How well-supported is this idea? One source or twenty?
- Contradiction: Do sources disagree? Which ones?
- Provenance: Where exactly did this idea come from?
That's the foundation for AI that doesn't just retrieve information but reasons about how reliable it is.
Current state: AI assistants can query the system via standard protocols (MCP). Future state: The knowledge graph becomes part of how AI thinks, not just something it queries.
As a human:
- Search your documents by meaning, not just keywords
- See how ideas connect across different sources
- Find where your sources contradict each other
- Trace any claim back to its origin
As an AI agent:
- Query persistent memory that survives across sessions
- Get grounded answers with confidence levels
- Reason about contradictions and uncertainty
- Build knowledge incrementally over time
- How It Works - The conceptual model (still no code)
- Glossary - Terms explained in plain language
- Using the System - Getting started as a user
- Operating the System - Deploying and maintaining