The opportunity Every company has knowledge scattered across documents, wikis, shared drives and inboxes. New staff ask the same questions, and experienced staff spend time answering them. A knowledge assistant built with retrieval-augmented generation (RAG) lets your team ask questions in plain language and get answers drawn from your own content, with links to the sources. This blueprint shows how we would build one. What leading platforms do RAG has become the standard approach for company-specific AI assistants. Instead of retraining a model, the assistant searches your documents for the most relevant passages and writes an answer based only on them. Mature implementations focus on three things: keeping the document index up to date, respecting who is allowed to see what, and always showing sources so people can verify the answer. Many workplace software products now include assistants built on this approach. Key features Ask questions in plain language Answers with links to source documents Connects to shared drives, wikis and PDFs Respects existing access permissions Keeps the index updated automatically Works in a web app or chat tools Feedback buttons to improve answers Usage reports showing common questions How it is built 1 Document connectors Import content from your drives, wiki and document stores on a schedule. 2 Indexing Documents are split into passages and converted into embeddings so they can be searched by meaning. 3 Retrieval with permissions Each question finds the most relevant passages the user is allowed to see. 4 Answer generation A language model writes a concise answer from the retrieved passages and cites them. 5 Feedback loop Ratings and unanswered questions show where documents need improving. Typical technology OpenAI Python Vector database Laravel React MySQL Build stages Stage 1 Pick the knowledge Start with one area, such as HR policies or product documentation. Stage 2 Clean and connect Remove outdated documents and connect the sources. Stage 3 Pilot with a team Let one team use it, collect feedback and measure accuracy. Stage 4 Roll out Add more sources and teams once answers are reliable. Risks and how to manage them Outdated documents Refresh the index automatically and retire old content. Seeing restricted information Apply the same access rules as the source systems. Over-trusting answers Always show sources so people can check them. Frequently asked questions What is RAG? Retrieval-augmented generation: the AI first finds relevant passages in your own documents, then writes an answer based on them. Does the AI learn from our private data? In a RAG setup the model is not retrained on your data. Your documents are searched at question time, and you control where they are stored. Which documents can it use? Common sources include PDFs, Word documents, wikis, shared drives and help centres, as long as they can be accessed by an API or export. Build it with CodecFactory Our Generative AI & Automation team can take this blueprint from idea to launch. We start with a free consultation and a written, fixed quote. Get a Free Consultation More blueprints → This blueprint is a research guide based on our study of leading platforms and public information. It is not a client case study. Platform and product names belong to their owners.