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RAG systems with answers you can check

Retrieval over your own contracts, tickets and databases, with a scored evaluation set that proves every answer traces back to a document.

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Blueprint of a filing cabinet with one drawer open, cables running out of the folders onto a routing line

What you get

  • An ingestion pipeline over your PDFs, exports and databases
  • A chunking and embedding strategy you can inspect
  • A vector store on your own infrastructure
  • Citations, permission filtering and a scored evaluation set

How it works

  1. 1

    Working session

    Thirty minutes. You bring one process, we map what it would take.

  2. 2

    Assessment

    One to two weeks. We measure the baseline and rank the options.

  3. 3

    Build

    Four to eight weeks. We ship into production on your stack.

  4. 4

    Run

    Monthly and optional. Monitoring, evaluation runs, upgrades, the next use case.

Questions

How is RAG different from fine-tuning a model?+

Fine-tuning changes how a model writes. Retrieval changes what it knows right now. For business content that updates, retrieval is almost always the right tool, and it can cite its source.

Our documents are scanned PDFs. Does that work?+

Usually, with extraction quality as the deciding factor. We test real samples during the assessment. Where scans are too poor to extract reliably, we say so rather than promising in advance.

How do we know the answers are accurate?+

The scored evaluation set: real questions, expected answers, expected source passages, scored on both retrieval and the final response. You re-run it after every change and read the diff.

Related

  • Conversational AI & Support Assistants→
  • AI Integration→
  • Healthcare & Life Sciences→

Bring one broken process.

No discovery marathon. Bring one process that costs your team hours and we will map what an integration would look like in a free 30-minute working session.

Book a working session→