Service · Aarohii AI Solution Private Limited
RAG development
Retrieval-Augmented Generation (RAG) retrieves relevant passages from your sources and gives them to a language model before it answers. We build RAG when the product must cite, not invent.
When RAG is the right default
Policies, product docs, tickets, and contracts change. Training those facts into a model is slow and often wrong. RAG keeps the source of truth outside the weights. See RAG vs fine-tuning for SaaS.
What the work includes
- Chunking and metadata that match how operators search.
- Retrieval quality tests (did we fetch the right passage?).
- Answer tests (did the model stay inside the passage?).
- A way to show sources in the UI.
Learn more: What is RAG? · RAG architecture.
How an engagement runs
- 20-minute fit call — we say yes or no.
- Written plan — scope, success tests, and what we will not do.
- Build with evaluation hooks and weekly demos you can inspect.
- Support after launch — we stay for the messy month, not just demo day.
Open the proof without an NDA: Pixellpeep, Captverse, Auvora. Buying notes: what drives cost · how to choose a company.
Questions
Do you guarantee zero hallucinations?
No. RAG reduces made-up facts when retrieval is good and the UI shows sources. We measure remaining errors; we do not pretend they are zero.
Which vector database do you use?
Whatever fits the client’s cloud and ops. The database is not the product. Retrieval quality and evaluation are.
If the problem maps to work we actually ship, we will say so in 20 minutes.
Request a fit call