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.
What you can build Build an agent that's an
Build an agent that's an
expert in your domain.
Point OWLGraph at the sources that matter to you — docs, wikis, archives, transcripts — and get a knowledge service your agent plugs into. Your AI answers from your knowledge, and shows where every answer came from.
Builder's gallery
One pattern, many experts.
The shape is always the same: your sources in, a domain-expert agent out. What changes is whose knowledge you load.
01
A support agent that knows your product
Point OWLGraph at your help docs, release notes, and internal runbooks. Your support agent answers from what your team actually wrote — and links the user to the exact passage it came from.
02
An internal expert on your company’s knowledge
Policies, contracts, wikis, meeting notes. Cross-document questions that span teams get answered from the relationships in the graph — not "here are four similar chunks."
03
A domain copilot for regulated or high-trust work
Legal, medical, financial, professional services — anywhere a wrong answer costs trust. Every claim carries a chain of cited facts your users can audit for themselves.
04
A research assistant over a specialist corpus
Feed it a literature base, a standards library, or an archive. Ask the multi-hop questions a keyword search can’t answer, and get the source passage behind every hop.
05
A knowledge layer for the agent you already have
You’re not switching stacks. OWLGraph is MCP-native — Claude, GPT, or your own agent plugs into the endpoint with one config block and starts answering from your sources.
06
A production copilot — the way we do it
FiveLoaves, our own sermon-prep product, runs on OWLGraph in production. Every theological claim traces to a verifiable path through the graph. We’re customer zero.
Why it holds up
The questions your users actually ask.
Multi-hop and constraint questions are where similarity search plateaus — and where your users notice when the answer is wrong. These are the questions the typed graph is built for.
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.
Have a corpus
in mind?
Start free and point OWLGraph at your sources. About 90 seconds to a working graph — no schema authoring required.