Retrieval that reasons, not guesses.
Vector RAG fetches by similarity. OWLGraph traverses a typed knowledge graph — supplier → ships_to → state ⊆ licensed-states — and returns the answer with the chain. Multi-hop questions stop being a guess.
AI infrastructure · v1.0 generally available Ship AI you
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OWLGraph turns your documents into a typed knowledge graph your AI reasons over — instead of guessing across chunks. Every answer traces back to its source. No hallucinations, no black box, ready for production.
Powering production AI at FiveLoaves+ 2 reserved
See it work Retrieval that shows
Retrieval that shows
its work.
OWLGraph traverses a typed knowledge graph and returns every edge it walked. Multi-hop questions stop being a guess — they become a traceable proof.
Entities resolved by type, not keyword
Edges chosen by the schema, not by similarity
Source passage cited per hop
Retrieval trace Trace · live
Question
Which suppliers ship to states where we're licensed?
01 Acme Corp → ships_to → California q3.pdf:14
02 California ⊆ LicensedStates inferred
03 Beta Inc → ships_to → Texas q3.pdf:22
04 Texas ⊆ LicensedStates inferred
Answer Acme Corp, Beta Inc
OWLGraph · 2 hops · 4 nodes Vector RAG returned 4 unrelated chunks
How it works
Stop your AI from guessing.
Three things change when retrieval becomes structured. None of them require you to learn a new query language.
Every answer carries an evidence chain.
OWLGraph emits a structured trace for every retrieval: entities resolved, edges traversed, source passages cited, confidence scored. Show it to your users. Audit it in production. Replay it in evals.
Drop into your stack in 10 minutes.
MCP-native. Works with the agent you already have — Claude, GPT, or your own. One config block, no rewrite. Bring your embeddings if you want them; OWLGraph uses them when they help.
In your code
from owlgraph_core import sdk as owl
db = owl.connect(
database_id="your-database-uuid",
api_key="sk-owl-...", # or set OWLGRAPH_API_KEY
)
result = db.retrieve("Which suppliers ship to states where we're licensed?")
print(result.answer)
for p in result.passages:
print(p.source, p.text[:200]) Where vector RAG breaks
The questions your users actually ask.
Multi-hop and constraint-based queries are where evals plateau. They're also where customers notice when the answer is wrong.
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.
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.
XS Instance Free 2 GB RAM · 1 CPU
First instance free, forever.
Compute from $0.25/hr per instance
Scale to zero anytime.
Storage $0.10/GB/mo
Pay for what you store.
Stop your AI
from lying.
Free tier. No credit card. 10 minutes to a working pipeline that traces every answer back to source.