Knowledge Management

Ask a product manager in a financial technology company a simple question — “How does this feature behave under that framework?” — and the answer may live in five places, three teams, two stale wikis, and one acronym that means different things depending on who had coffee with whom. This is the everyday enemy of enterprise AI. Not lack of models. Not lack of dashboards. Not even lack of documents. The problem is that internal knowledge rarely behaves like a neat public benchmark. It is fragmented, duplicated, partially obsolete, acronym-heavy, and governed by access rules that make the usual “just send it to a cloud assistant” suggestion both naïve and professionally adventurous. ...

TL;DR for operators Most enterprise RAG failures do not begin at the chatbot. They begin earlier, when the retrieval system slices policy manuals into arbitrary chunks, flattens tables into textual porridge, ignores metadata, retrieves semantically similar but operationally wrong passages, and then asks an LLM to look confident. Naturally, the LLM obliges. It has excellent manners. ...

TL;DR for operators Enterprise RAG does not fail because the chatbot forgot to sound confident. It fails because the answer is often scattered across the least glamorous parts of the company: Slack threads, meeting transcripts, pull requests, document revisions, customer reports, employee metadata, and URLs somebody pasted into a chat six weeks ago. ...