The default enterprise AI plan is "we have tons of data, let's point an LLM at it." The default outcome is a demo that impresses in the room and collapses in production — because the model was handed a landfill and asked to be a librarian.
A model is only as smart as what it can reach. Raw volume — feedback tickets, call transcripts, billing lines, activity logs — is noise until something links it: this complaint to that account, that account to this revenue, this spend line to that team. The unglamorous work of turning records into a graph of entities and relationships is what makes "what are customers saying?" answerable as "what is it costing us?"
That's why knowledge graphs quietly became the most valuable substrate in the LLM era. Retrieval finds you similar text; a graph gives the model something to reason over — who, which account, how much, connected to what. The taxonomy tells you what's being said. The graph tells you who's saying it and what it's worth.
So before the agent roadmap and the copilot mockups, ask the boring question: can a model actually query our world? If the answer is no, that's the product to build first. Structure beats volume, every time.
Built something like this? I'm always happy to compare notes.
aniket.kgp25@gmail.com →