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RAG pipeline review & sprint

A retrieval-augmented generation (RAG) pipeline answers questions from your own documents and shows where each answer came from. We review the one you have, or design a new one, and measure it against your real questions so quality is shown and not assumed.

What you get

When this fits

  • You have documents or data you want an AI assistant to answer from.
  • A prototype gives plausible answers, but you cannot tell how often they are right.
  • You need every answer to carry its sources, for users or for an audit.

How it works

01

Look at the questions

We start from the questions your users actually ask, and the documents that should answer them.

02

Tune and measure

Chunking, indexing and ranking are adjusted and scored against an evaluation set built from those questions.

03

Hand over

You receive the pipeline or the design, the evaluation set to re-run it, and the audit trail it produces.

Common questions

What is a RAG pipeline?
Retrieval-augmented generation first retrieves the passages of your documents that are relevant to a question, then has a language model answer from them. Because the answer is built from retrieved text, it can cite its sources.
Why does it need an evaluation set?
Without one, a change that improves one answer can quietly break another. An evaluation set of real questions turns “it seems better” into a measured result you can re-run after every change.
Can the pipeline run on private models?
Yes. A RAG pipeline works with a commercial API or with open models on your own infrastructure. See private AI implementation for the second case.
How long does it take, and what does it cost?
The sprint is fixed scope and typically two weeks. You receive a one-page written scope first, and nothing starts until you agree to it.

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