I'm not talking about the obviously ridiculous AI hallucinations. I'm talking about those answers that looked completely believable until someone actually checked the numbers.
Maybe it was a calculation, a misleading chart, or a metric that meant something entirely different from what the AI assumed. What happened, and how did you catch it?
Saving what the team means by “sales” is the detail that stood out. It gives people something concrete to check when two reports disagree.
@oleksii_sekundant it's made a huge difference to both accuracy and usability of the whole platform. You can't run a business on AI without trust!
Really like this approach. Building AI around a business’s actual context rather than just giving it access to data feels like a meaningful step forward. Good stuff!
@stephen_doyin yes, that's what separates Querri from other data tools because now Querri can give accurate and relevant responses as opposed to spitting out numbers that dont make sense.