Built for teams who
can't ship AI that lies.
We believe the database should understand your data model — not the other way around.
The Problem
Teams ship a RAG demo that dazzles, then watch it fall over in production. Vector search retrieves by similarity — it can't reason about how your facts connect, so multi-hop and constraint questions come back wrong, confidently. Every wrong answer that reaches a user costs trust you can't easily rebuild.
Our Approach
OWLGraph turns your documents into a typed knowledge graph — entities with types, relationships with rules. Your AI reasons over that structure instead of guessing across chunks, and every answer carries an evidence chain back to the source passage.
You bring a corpus; OWLGraph induces the schema for you in about 90 seconds. No schema authoring, no query language to learn. The intelligence lives in the database, and every answer shows its work.
Why it's different
Reasoning at query time
OWLGraph reasons over types and relationships — class hierarchies, transitive links, constraints — so the right answer to a multi-hop question is a traversal, not a guess.
Inference at write time
Derived facts are computed when data is written, not at query time. Reads stay fast no matter how rich your schema gets.
MCP-native
Point the agent you already have — Claude, GPT, or your own — at OWLGraph over MCP. One config block, no rewrite.
Fully managed
A hosted graph database with nothing to operate. Bring a corpus, get a typed graph and an endpoint. Scale when you're ready.
Built on OWLGraph
FiveLoaves.ai is a sermon-prep tool that stores the Bible as a typed knowledge graph on OWLGraph. Every claim its AI makes traces back to a verifiable path through the graph — not an LLM hallucination. It's how we dogfood OWLGraph against real-world complexity in production.
Try OWLGraph
free.
One XS instance, no credit card, no time limit. Point it at a corpus and watch the graph build.