The full pipeline from Notion or Help Scout or Confluence to a chat agent that does not hallucinate. Chunking, embeddings, freshness checks, eval loops.
The chat agent demos beautifully because the demo content is curated. Then you point it at your real Notion or your real Help Scout, and the answers degrade in week one. The reason is almost always the same: the pipeline from KB to retrieval is naive. Below: the full step-by-step that prevents the degradation. The 72-hour deploy plan We ship most projects in this category on a 72-hour clock.
Interview the two people who do this job today. Pull 50 sample inputs (calls, chats, tickets). Identify the three top customer intents. Stand up the eval harness with 25 cases drawn from the sample inputs. The eval harness has to exist before the first prompt does.
The full pipeline from Notion or Help Scout or Confluence to a chat agent that does not hallucinate. Chunking, embeddings, freshness checks, eval loops. It is filed under AI Infrastructure because that is where operators looking for this problem actually start, and it is written from production work rather than from a content calendar.