Enterprise RAG
Retrieval-augmented generation over your documents, policies and product data — so AI answers from your facts instead of its training.
RAG connects a language model to your live knowledge through a vector index, which is what you want whenever the underlying facts change. We build the whole path — ingestion, chunking, embedding, retrieval and grounded generation — with the discipline that matters most in production: the model cites what it found and says it does not know rather than filling the gap.
What this solves
- AI answering from general training rather than your documentation
- Knowledge spread across wikis, PDFs and people’s heads
- Answers going stale the moment a document changes
- No way to see why the model said what it said
What you get
- Document ingestion and chunking pipeline
- Vector index with a re-indexing path as content changes
- Grounded generation with source attribution
- Explicit "not found" behaviour instead of invention
- Evaluation set to measure retrieval quality
Technology
Where we've deployed it
Mukunda Jewellers
Retail · Jewellery
An outbound AI agent that calls recent customers of a ten-branch jewellery chain, collects genuine feedback on their purchase, then introduces the gold savings schemes most relevant to them — in English, Telugu or Hindi.
Rankridge IIT-JEE Excellence
Education · Competitive exam coaching
A website chatbot answering IIT-JEE, NEET and EAMCET admission questions from a curated knowledge file, capturing visitor leads — alongside a Next.js site built for local search.
Frequently asked
What is the difference between RAG and LLM fine-tuning?
RAG connects a model to your live data through a vector database and is the right choice for knowledge that changes — documents, policies, catalogues, internal wikis. Fine-tuning bakes domain language and behaviour into the model weights and is the right choice for specialised jargon, consistent formatting, and cutting inference cost at high volume. We recommend RAG for most use cases and fine-tuning where volume or language specificity justifies it. They combine well.
Talk to the engineers who build it
Tell us the problem and we'll tell you whether enterprise rag is the right answer — and what it would take.