RAG system development
Retrieval-augmented generation finds the relevant passages in your documents and databases, then has a language model answer from them. We build the ingestion, retrieval and evaluation as a system you own.
A person asks in plain language.
The system finds the passages that person is allowed to see.
The model answers from those passages and links to them.
No passage found means no answer, not a guess.
01 / Is this the one
RAG suits knowledge that is written down and changes. It is the wrong tool when the answer lives in a live system or the documents cannot be trusted.
02 / What the work covers
The model is the smallest part. Most of the work is in the content, the search and the testing.
What you have, how current it is and who may see it. This decides more than the model does.
Documents parsed, split and indexed. Keyword and semantic search, filtered by each user’s permissions.
The model keeps to the retrieved text, shows its sources and says when nothing was found.
Real questions with expected answers, run before release and after every change.
03 / Limits and risks
A model can still misread a passage. Citations let a person check, and for high-stakes answers a person should.
Scanned PDFs, tables and duplicate versions lower retrieval quality. Expect clean-up work on the documents.
The honest number comes from testing on your questions. We agree the target with you, then measure.
04 / Built and operated by NSDBytes
Our own AI product. India market news as short briefings on Telegram and Web Chat, each story linked to the listed company, commodity, index or currency it concerns. It shows a team that processes a large volume of text every day and ties each item to the thing it is about.
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Add this address to Claude, Cursor or any MCP client.
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A client project. FocusPulse is not a document-retrieval system, and we have no published client case study for RAG.
05 / What to do next
The first step is a free scoping call. Bring ten real questions and a sample of the documents that should answer them. A written estimate follows.
A little clarity
A retrieval-augmented generation system looks up relevant passages in your own content and gives them to a language model to answer from. It is worth building when people need answers from documents that change, and need to see the source. It is not needed for a handful of short documents.
Chunking splits long documents into passages small enough to retrieve one at a time. Embeddings turn each passage into numbers that capture its meaning, so similar ideas sit close together. A vector database stores those numbers and finds the closest passages to a question quickly.
Yes. Retrieval is separate from the model that writes the answer, so the model can be changed. Each change still needs the evaluation set run again, because a different model can read the same passages differently.
We have no published client case study for a RAG system, and we would rather say so. Our AI engineering is shown in FocusPulse, our own product, which we build and operate. For a client system we recommend a pilot on a small set of real questions, so you judge measured results.
It depends mostly on the volume and condition of your content and on how many systems it lives in. First releases at NSDBytes start from $5,000. The project cost estimator gives a range for an AI build, and a fixed quote follows a free scoping call. Hosting and AI model usage are billed by their providers and are separate.
Start wherever you are
And a sample of the documents that hold the answers. We’ll tell you whether retrieval fits and what a pilot would take.