What makes iDBQuery different from a normal AI chatbot?

Question

iDBQuery · Accepted Answer

Unlike a normal AI chatbot, iDBQuery answers only from your real data and cites every number back to its exact source row. A chatbot generates plausible text with no way to verify it; iDBQuery writes and runs real queries against your connected sources and shows its work, so the answer is auditable. A general AI chatbot is a conversationalist. It produces fluent, confident text — but it has no connection to your actual data and no way to prove what it says. Ask it about your revenue and it will guess, hallucinate, or refuse.

iDBQuery is built the opposite way:

- Grounded in your data — it connects directly to your databases, files and APIs and answers only from what's really there
- It runs real queries — it writes SQL, executes it against your live model, and returns actual results
- Every number is cited — figures trace back to the exact source rows, so you can verify them
- It investigates — an autonomous Analyst agent can dig into a question on its own, not just respond to one
- It understands messy schemas — a semantic layer decodes cryptic column names so it gets your data right

So the difference isn't a better chat experience — it's a fundamentally different contract. A chatbot says "here's something that sounds right." iDBQuery says "here's the answer, here's the query I ran, and here are the rows it came from." That's the gap between a demo and a tool you can put in front of a board.

Qu'est-ce qui distingue iDBQuery d'un chatbot d'IA classique ?

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