Why use Heidi instead of building our own RAG pipeline?
RAG is retrieval; Heidi is memory plus a runtime. Building your own means owning ingestion, permissions, consolidation, forgetting, agents, scheduling, and audit forever. Heidi ships all of it, hosted, for less than one engineer-week per month.
Teams that build RAG in-house discover the hard part is not the vector store, it is everything around it: keeping facts current, honoring per-person permissions, deduplicating truth, deleting on request, and proving what the AI did.
That surrounding system is Heidi's whole product. If you have an ML team, point them at your product instead, and read the brain over the API like every other tool.