RAG & Knowledge AI

RAG, or retrieval-augmented generation, is a method where an AI model first retrieves relevant passages from your own documents and then writes an answer using them. This lets it answer questions about your business without being retrained, and it can cite its sources.

RAG keeps knowledge in your documents, so you can update it instantly, control who sees what and show sources. Fine-tuning changes the model itself, which is slower to update and does not give citations. Many systems use RAG first and add fine-tuning only for style or specialist tasks.

RAG reduces made-up answers by grounding them in your documents, but it does not eliminate them. We require citations, verify answers against sources, tell the model to say when nothing relevant is found and measure correctness on a test set.

We attach access permissions to every passage in the index and filter results by the user’s rights before the model sees anything, so answers never draw on documents the person could not open.

PDFs, Word files, web pages, wikis, tickets, emails, spreadsheets, slides and databases. Scanned documents are read with OCR, and tables and images are handled with extra steps.

Usually some form of vector search helps, often combined with keyword search for exact terms. We choose between managed services, databases you already run and open source options based on your scale and rules.

We design the setup with your security team. Options include keeping everything in your own cloud account, using providers with suitable data terms, or running models inside your environment for stricter needs.

We build a test set of real questions with agreed good answers, then score retrieval, answer correctness and citation accuracy. We track the same measures after launch.

A working prototype on one document set often takes two to four weeks. A production system with permissions, updates and evaluation usually takes two to four months.

We connect to your sources so new and changed documents are re-indexed automatically, and review questions the system could not answer to find content gaps.