Knowledge AI for business: RAG & citations

Knowledge AI based on RAG (retrieval-augmented generation) means that a language model answers questions not from its training data but from your own documents: manuals, contracts, standards, tickets or wikis are indexed, relevant passages are retrieved for each question and the answer is generated with a citation. What matters is the citation down to page and section – only then can employees verify an answer and use it responsibly in the business. Scattered documents become a knowledge base that employees can query in natural language.

This matters for mid-sized companies and corporations now because knowledge sits in people’s heads, PDFs and shared drives and can be lost with every staff change. Public chatbots are off-limits for sensitive data, and answers without a source are worthless. Typical mistakes are chatbots that invent plausible-sounding answers, missing permission and tenant separation, and unclear data flows outside the EU. In law firms, clinics or critical infrastructure, such a project then fails on data protection and compliance. And without quality measurement, nobody knows how often the system is right.

I built my own knowledge AI for GDPR-compliant operation, anirag.io, which backs every answer with a source down to page and section – no source, no answer. The platform indexed 12,000 PDFs in around 30 seconds and answers with a p99 latency under 800 ms. An LLM router chooses between Anthropic, OpenAI (EU), Mistral and Aleph Alpha, and on-premise is possible for critical infrastructure, clinics and law firms. After the source audit, the first knowledge space is typically ready in weeks two to three. DPA and TOMs are included.