Fluent is not the same as correct
Ask a general-purpose AI about your business and it will answer — smoothly, in full sentences, with total confidence. The problem is that it's guessing from patterns, not reading your numbers. For a demo that's fine. For a decision, a fluent wrong answer is worse than no answer, because it's easy to believe.
Grounding is the whole game
A useful business AI isn't a cleverer chatbot. It's one that can only answer from your actual data, and says so when it can't. That constraint is the feature.
- It reads your records: answers come from your orders, invoices and history, not the open internet.
- It shows its source: you can trace an answer back to the rows it came from.
- It admits the gap: when the data doesn't cover a question, silence beats invention.
Why the data comes first
This is the same reason we spend most of a migration on the data, not the model. An AI grounded in messy records still gives messy answers — it just delivers them more confidently. The clean, structured data underneath is what turns a plausible-sounding tool into one you can actually run decisions on.
Start with a question you can check
The honest way to test any of this is to ask something you already know the answer to. If the system can tell you last month's real numbers, with the records to back them, it can be trusted with the questions you can't check yet. If it can't, no larger model will fix that — the work is in the data.