RAG & Knowledge
Retrieval-augmented systems over your own documents and data. Internal Q&A, document assistants, and knowledge search that answers accurately from your content.
Overview
RAG turns your documents into an answer engine. We ingest, embed, and index your content — contracts, manuals, policies, support history — and build a question-answering layer that grounds every answer in a citation. It's how you give an LLM your business context without retraining a model.
What we deliver
- 01checkIngestion pipeline — PDFs, web, databases, ticketing systems
- 02checkHybrid retrieval (semantic + keyword) with reranking
- 03checkQuestion-answering UI with source citations and confidence
- 04checkAccess control — who can ask, who can see what
- 05checkEval harness — measured accuracy on your real question set
- 06checkRefresh schedule — keep the index current as content changes
How we work
Source mapping
Where the knowledge actually lives. Access, permissions, freshness.
Ingest & index
Chunk, embed, store. Hybrid retrieval — semantic + keyword for proper-noun precision.
Evaluate
Run against a real test set. Tune until the accuracy bar is consistently hit.
Ship & maintain
Ship the UI, wire access control, schedule the refreshes. Care plan keeps it accurate.
Capabilities
Have a knowledge base nobody reads?
We'll turn it into something people actually ask questions of. Scoping call gets the first numbers on the table.
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