Retrieval-augmented generation over your documents, wikis, and data: find the right evidence, ground the answer in it, and cite the source. The retrieval layer that powers assistants a business can trust.
Companies sit on wikis, PDFs, tickets, and docs that no one can search across. Keyword search returns a list of links; people need an answer — with the source, so they can trust it.
Enterprise search done right isn’t a search box that returns ten blue links — it’s a system that reads your knowledge and answers questions from it, with citations. We build the retrieval layer properly: content is chunked to its natural shape, indexed with hybrid vector-and-keyword search, and re-ranked so the model always sees the most relevant evidence first. The answer is grounded strictly in that evidence and returns inline citations, so every response is verifiable and nothing is a black box. Metadata filters and per-tenant isolation keep each user scoped to what they’re allowed to see. This is the same retrieval discipline behind our 130 production agents — the layer that makes AI answer from truth instead of guessing.
The pipeline that turns a pile of documents into cited, trustworthy answers.
We tune the pipeline to your content — chunking, hybrid search, and re-ranking — so the model always sees the most relevant evidence before it writes a word. Better retrieval is what makes RAG accurate.
The answer is grounded strictly in retrieved evidence and returns inline citations — so a user can click through to the exact document and paragraph. A faithfulness check gates answers that stray from the source.
The RAG loop — retrieve the evidence, ground the answer, cite the source, verify it’s faithful.
This is our production RAG discipline — retrieval, grounding, citations, and faithfulness evaluation — with a human in the loop. The proof is our own operation: every one of our 130 production agents answers on this retrieval layer, across 18 live platforms, and captures what it learns back to its knowledge base.
Delivered by the retrieval fleet that grounds our own agents.
The real, relevant stack we build this with — model-agnostic, open-source-first, production-grade.
Every technology below is delivered by the same composed engineering team.
Tell us what you need searchable. We’ll build the retrieval, grounding, and citation layer that turns your documents into answers you can trust.
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