Your RAG system is probably a search problem wearing an AI costume

Most broken RAG projects are not broken because the model is dumb. They are broken because the source content is messy, the retrieval strategy is vague, permissions are duct-taped on later, and nobody defined what a correct answer looks like before the demo got applause.

A competent architecture starts with boring questions: who is allowed to see which documents, how freshness is enforced, what gets logged, how answers are evaluated, and where a human can override the machine. If those answers are fuzzy, the system is not production-ready. It is an expensive autocomplete box.

Knolo exists because that boring layer has to be a product: a Knowledge Image, lexical retrieval first, citations, ACLs, hashes — and, in V5, a runtime that can prove the run. Models are replaceable. The knowledge operating layer is not.

In a Bootcamp we will tell you whether the retrieval idea is worth pursuing, where it will break, and which assumptions need to die before they eat the Deploy budget.