Answers from your documents, with sources, for the right people
An LLM on its own does not know your policies, products or procedures. RAG fixes that: before answering, the system looks up the relevant passages in your documents and gives them to the model, which answers from them and shows where the answer came from. Done well, it is the most useful thing most organisations can build with AI.
Ask a question, and watch what happens
Choose who is asking and a question. Watch the system search, remove documents the user is not allowed to see, prefer current documents over archived ones, and answer with sources. Then ask the salary question as an employee and as an HR manager.
Demonstration data only.
Why document chatbots disappoint
Wrong or outdated answers
Old versions of a policy sit next to new ones, and the system cannot tell which is current.
Confidential documents leak
Everything was indexed together, so anyone can get answers drawn from HR or board documents.
Tables and scans are lost
PDF tables, scanned forms and slides were converted badly, so the facts in them are never found.
Nobody knows if it is good
There is no test set, so every change is judged by whoever tried a few questions that day.
Guides and examples
How RAG is built
The two pipelines: preparing documents, and answering questions.
Read the guide →Preparing documents
Parsing PDFs, tables and scans, splitting into passages, keeping versions current.
Read the guide →Better retrieval
Hybrid search, reranking and the choices that decide answer quality.
Read the guide →Permissions
Making sure people only get answers from documents they are allowed to read.
Read the guide →Measuring answers
Test sets, scores and how to know a change made things better.
Read the guide →Use case: HR policy assistant
An illustrative scenario for 3,000 employees, in English and Hindi.
Read the use case →RAG questions, answered
Vector databases, accuracy, languages, cost and more.
Read the FAQ →Related: enterprise LLM for choosing and hosting the model, and agentic AI when answers should turn into actions.
How a RAG engagement runs
- Collect real questions. 100 to 200 questions people actually ask, with the right answers and source documents.
- Map the documents and their owners. Where they live, who may read them, which versions are current.
- Build ingestion carefully. Good parsing of PDFs, tables and scans, with permissions carried along.
- Tune retrieval against the test set. Chunking, hybrid search and reranking, measured, not guessed.
- Pilot with one department. Measure answer quality and deflected questions, and fix gaps in the documents themselves.
- Run it. Daily document sync, monthly quality checks and a clear owner for every source.
Want answers from your own documents?
Tell us where the documents live, who should be able to ask, and ten questions people ask today. We will come back with a plain view of what it takes to answer them well, and safely.