RAG System Development
RAG system development
We build retrieval-augmented systems that answer questions from your own documents — with citations and the honesty to say when they do not know.
The problem
Your organisation has documents. Product manuals, HR policies, compliance guidelines, past proposals, archived contracts. When someone needs an answer that is in there, they either search and scroll for ten minutes, ask a colleague who read it once, or give up and write something that may be wrong. The knowledge exists. The problem is that finding it takes longer than the answer is worth, so people stop trying.
Your documents become answerable. Answers come with the source they came from.
Built for: Teams with a document library — policies, manuals, contracts — that people currently search by hand.
What we deliver
Retrieval quality first. A RAG system is only as good as its retrieval step. We measure retrieval precision and recall against real questions from your team before we connect the language model to it.
Cited answers. Every answer links back to the specific document and section it came from, so a reader can verify it rather than just trust it. That matters more than it sounds when the document is a compliance policy.
Refusal when the answer is not there. If the question cannot be answered from your documents, the system says so instead of confabulating a plausible-sounding response. We treat false confidence as a harder failure than a polite I do not know.
Document update handling. Documents change. We build the ingestion pipeline so adding, updating or removing a document is a routine operation, not a rebuild.
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Tell us what’s slow.
Describe the job eating your team’s day. We’ll tell you straight whether an agent is the right fix — and if it isn’t, we’ll say so.
Frequently asked questions
- How many documents can the system handle?
- Scale is not usually the binding constraint. What matters more is document quality — a system trained on well-structured PDFs retrieves better than one trained on scanned images of handwritten notes. We assess your document set before quoting so you know what retrieval quality to expect.
- Can it handle documents in Hindi or regional languages?
- Retrieval over Hindi documents works well with multilingual embedding models. Mixed-language documents — where the same document switches between English and Hindi — need more careful handling, and we will tell you if your document set falls into that category.
- If our documents contain confidential information, where does that data go?
- The documents are processed and stored in your own cloud environment. They are not sent to any third-party service except the model API call, and we can structure that to stay within Indian-region infrastructure if your data residency requirements need it.