Content
- Building a company brain: from document chaos to a reliable answer You build a company brain in stages, not with one large import: inventory the sources, pick the authoritative version per topic, connect systems and set access rights, ground every answer in sources, and maintain the whole thing in operation.
- Conversational AI: the building blocks behind a good assistant A good assistant is not a clever model with a chat window.
- Copilot or your own assistant: what fits when A standard copilot fits when you want to move faster in daily office work and little proprietary knowledge is involved.
- Why internal AI assistants fail on data, and how RAG helps An internal AI assistant almost never disappoints because the model is weak.
- Rolling out an AI assistant in the team: what adoption really takes An AI assistant only creates value once the team uses it in daily work.
- Business AI assistant: build or buy? Buying fits standard tasks with little integration.
- Internal AI assistant and data protection: what to clarify before the build An internal AI assistant almost always processes personal data, so before the build it needs a clarified legal basis, clean access rights, a defined logging policy and real data minimisation.
- Reducing hallucinations in an AI assistant An AI assistant hallucinates because a language model produces plausible text and has no built-in truth checker.
- RAG explained simply: how an assistant answers from your data RAG means an assistant does not answer from memory but first searches your documents for the passages that fit the question, then builds the answer from exactly those passages, source included.
- Preparing the knowledge base for an AI assistant An AI assistant answers only as well as the sources you prepare for it.
