RAG & Document Intelligence
Answers your documents can back up
Your knowledge is in PDFs, contracts, tickets, emails and scans. We turn that into a retrieval layer that answers with citations and an extraction pipeline that turns documents into structured records. Chunking, embeddings, hybrid search and reranking are tuned against your corpus and measured, because retrieval quality — not the model — is what decides whether the answer is right.
- Ingestion pipeline: OCR, layout parsing, chunking, metadata and incremental refresh
- Hybrid search (vector + keyword) with reranking, tuned on your data
- Structured extraction to typed records with confidence scores
- Classification and named-entity recognition at scale
- Citation-linked answers that point to the exact source span
- Permission-aware retrieval so users only see what they may see
- 01
Profile the corpus
Formats, volume, quality and update frequency decide the whole design.
- 02
Tune retrieval
We measure recall on real queries before a single answer is generated.
- 03
Ground every claim
Answers cite spans; unsupported claims are refused rather than invented.
Yes. We deploy inside your cloud account or VPC, use models with no-training data agreements or self-hosted open weights, and keep retrieval permission-aware so answers never cross a tenant or role boundary.
We have built retrieval over hundreds of thousands of pages. Scale is mostly an ingestion and indexing engineering problem; answer quality depends far more on chunking, metadata and reranking than on raw size.