DocuMind AI — Document Intelligence Platform

The team we built this for had eleven years of internal documentation — onboarding guides, incident postmortems, vendor contracts — spread across Google Drive, Notion, and a graveyard of old Confluence pages nobody trusted anymore. New hires spent their first two weeks just asking around because search never turned up the right doc, and when it did, nobody knew if it was still accurate. Support agents were quoting policy from memory half the time. That's a compliance problem waiting to happen, not just an annoyance.

We didn't start with the model. We started with the ingestion pipeline, because messy chunking produces messy answers no matter how good the LLM is. Documents get parsed, split with overlap-aware chunking, embedded, and stored in pgvector alongside the source PDF page, so every answer can point back to exactly where it came from. FastAPI handles the API layer with JWT-scoped multi-tenant access, Celery workers chew through ingestion jobs asynchronously so a 400-page manual doesn't block the request thread, and Stripe handles metered billing per seat. Langfuse sits underneath the whole thing, tracing every prompt and retrieval so we could actually see which queries were returning weak context instead of guessing.

Three weeks after rollout, the support team stopped pinging engineering to ask "is this still true?" — the agent's citations did that job instead. Ingestion that used to mean someone manually re-indexing a wiki now happens automatically the moment a document lands in the folder. It's not flashy, but it's the kind of infrastructure that quietly saves a team a few hours a week, every week, indefinitely.