RAG development makes a language model answer from your documents rather than from its training data, and then proves that it did.
We have shipped one, and we will show you the code. That evidence is worth more than a demonstration in a market where this is the most claimed capability going.
What RAG Development Actually Involves
Chunking that respects the document. Splitting on the structure a document already has, its sections, clauses and headings, is unglamorous and it is most of the quality.
Retrieval you can inspect. For any answer, which passages were pulled and in what order. That view is what makes a wrong answer diagnosable.
Citations that resolve. Every claim points at the passage it came from, and somebody can click it and read the original.
Refusal when nothing matches. The hardest part, and the one that decides whether people trust the system in month three. Silence beats a confident invention.
An evaluation suite. Real questions from the people who will use the system, with agreed correct answers, scored on whether the cited passage genuinely supports the claim rather than on whether the paragraph reads well. Fluency and accuracy come apart exactly where it costs you.
The Baseline Comes First
Before anything is built, we measure what a plain keyword search over the same corpus already achieves on your own questions.
That number is what RAG development has to beat, and it is higher than most people expect. Where the margin is thin, improving the search is the recommendation you get, established in week one at our cost.
Where the margin is wide, you have a business case with a figure attached rather than an impression.
Where the Cost Actually Sits
Not in the model. Recurring cost is dominated by how much context each call carries, and that is a design decision.
Retrieving twenty passages where four would do multiplies the bill by five and often reduces accuracy, because the useful passage sits buried among near misses. We measure that and tune it, and it is usually where the savings are.
You get the arithmetic at scoping, including the picture at ten times the opening volume, so the running cost is a decision rather than a discovery in month four.
What You Own at the End
The code, the prompts, the chunking configuration, the index settings and the evaluation set, in your repository from the first commit.
There is no subscription and no dashboard to log into. Good RAG development leaves you able to re-run the evaluation yourself and see the same score we did.
Tell us what question your people keep asking and where the answer currently lives.
Related Services
Whether it works is AI evaluation, and on this kind of system that is not optional.
Where the answer has to trigger an action rather than be read, that is AI agents. Where the documents stay on your own infrastructure, start at on-premise AI, and where the job is pulling structured fields out of documents, that is document automation.