Retrieval-augmented generation (RAG) lets an LLM answer from your own documents and data instead of guessing. We build RAG systems that let your team and customers chat with your knowledge base, contracts, or product data — getting accurate, grounded answers with sources.
Retrieval-augmented generation (RAG) lets an LLM answer from your own documents and data instead of guessing. We build RAG systems that let your team and customers chat with your knowledge base, contracts, or product data — getting accurate, grounded answers with sources.
A general LLM does not know your business and will confidently make things up. Meanwhile your real answers are buried across PDFs, wikis, and databases no one can search quickly.
A clear, proven path from where you are today to a working solution.
We process your documents and data and index them in a vector database.
The system finds the most relevant content for each question before answering.
The LLM answers from retrieved content and cites its sources, reducing hallucination.
Permission-aware retrieval so users only see what they are allowed to.
Common scenarios where our clients put this to work.
A real project we've shipped that shows this capability at work.
An all-in-one ERP that unifies accounting, POS, inventory, manufacturing, and retail in one platform — with one-click data migration from QuickBooks, Xero, Tally, and Wave, so businesses can switch without losing years of history.
Common questions about RAG & Chat With Your Data.
The model answers from retrieved, real content and cites sources, instead of relying on its general memory.
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