RAG & Knowledge Systems
Grounded answers from your private knowledge — accurate, cited, and access-controlled.
RAG & Knowledge Systems that earns its place in production
RAG is the difference between an LLM that guesses and one that answers from your truth. We build retrieval systems over your documents, wikis, tickets, and databases that return grounded, cited answers — with the chunking, indexing, and reranking choices that actually move accuracy.
We obsess over the unglamorous parts that decide quality: ingestion and parsing, hybrid retrieval, reranking, and an evaluation set built from your real questions. And we respect your permissions, so users only ever retrieve what they're allowed to see.
Key capabilities
Ingestion pipelines
Parse and chunk PDFs, wikis, tickets, and databases.
Hybrid retrieval
Vector + keyword + rerank for real accuracy.
Grounded answers
Citations and confidence, not confident guesses.
Permission-aware
Retrieval that respects your access controls.
Where it delivers
Support assistants
Resolve tickets from your help center and past resolutions.
Internal search
One grounded answer instead of ten stale wiki pages.
Compliance lookup
Cited answers from policy and regulatory documents.
Frequently asked
No. Retrieval is permission-aware — documents are filtered by the requesting user's access before they ever reach the model.
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