Which suppliers ship to states where we're licensed?
Why it's hard Two-hop: supplier → ships_to → state, then state ⊆ licensed_states.
How vector RAG fails Vector RAG returns four chunks about shipping or licensing — never the join.
Use cases The questions your users
The questions your users
actually ask.
Vector RAG fetches by similarity — it fails on multi-hop, constraint, and temporal questions. Here's what changes when retrieval reasons over a typed graph.
Where vector RAG breaks
Questions that stump similarity search.
Multi-hop and constraint-based queries are where evals plateau. They're also where customers notice when the answer is wrong.
Show me drug pairs approved for X but contraindicated with Y.
Why it's hard Constraint join across two schemas — therapeutic class + interaction profile.
How vector RAG fails Embedding similarity can't enforce "approved for X AND not in interaction set."
Which of our customers have been with us 5+ years and use product Z?
Why it's hard Temporal predicate + product attribute, both must hold.
How vector RAG fails Chunked text doesn't survive constraint composition; agent loops out before converging.
What teams build
Products, not "verticals."
OWLGraph isn't tied to a domain. Anywhere answers have to be right and auditable, the same shape shows up.
01
Regulated & professional-services assistants
Answers your users can audit, because every claim shows the chain of facts it came from — not a similarity score you have to trust.
02
Internal knowledge agents
Cross-document questions that span teams, where "four similar chunks" was never the answer. The graph carries the relationships your docs only imply.
03
Domain copilots
FiveLoaves built a sermon-prep copilot on OWLGraph: every theological claim traces to a verifiable path through the graph, not an LLM guess.
Have a use case
in mind?
Start free and point OWLGraph at your corpus. 90 seconds to a working graph — no ontology authoring required.