Vector RAG
- Text sliced into isolated chunks
- Retrieval by similarity, not meaning
- Plausible answers, approximate sources
- Permissions filtered after generation
- Re-index the whole corpus on change
- Breaks on cross-document relations
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Trusted by document-heavy teams in regulated industries
How it works
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Beyond vector RAG
Vector search retrieves look-alike text. GraphRAG reasons over a living knowledge graph — held in memory on Memgraph — so answers are related, real-time and verifiable.
For technical teams
Orpheam is not a black box. REST, MCP, webhooks, SDKs: everything is instrumented to live inside your stack, not next to it.
curl -X POST https://api.orpheam.com/v1/answers \
-H "Authorization: Bearer $ORPHEAM_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"workspace": "compliance-eu",
"question": "DORA obligations for a critical provider?",
"policies": ["compliance.read"],
"citations": true
}'News & insights
Production lessons, GraphRAG patterns and product updates — written for engineers and compliance teams.
Vector chunking unlocks demos. It collapses on the three questions a regulator always asks: where is the proof, who could see it, and can you replay it?
A manager, an employee and HR ask the same question and get the same engine — scoped to what each is allowed to see. Here is how access travels with the query.
Patch a single regulation and the graph follows — no full reindex. A look at delta streaming and version-per-entity in the Orpheam pipeline.
Get started with Orpheam
Bring your documents and your constraints. We prepare a tailored demo on your data and map a concrete path to production.