We've been building chatbots for years, and the same gap keeps showing up: dashboards tell you how much your bot did, not how well it did.
Conversations handled. Containment rate. CSAT. Response time.
All green. Meanwhile the bot spent last Tuesday confidently quoting a refund policy that changed in March to eleven people, none of whom filled out a survey.
That kind of thing only surfaces when someone happens to read transcripts, which mostly nobody has time to do.
Inquio
Hardest part for us was that the worst answers never look like failures. Frustration and risky responses at least leave a trace someone can complain about. The plausible and slightly wrong ones don't, the person just fixes it themselves and your quality metrics stay green. If your audit can flag those I think that's the whole product, and the rest is table stakes.
Inquio
@asadmalik901 Exactly. That’s one of the biggest problems we’re trying to solve.
The dangerous conversations are often not the ones where the bot clearly fails—they’re the ones where the answer sounds right but is subtly wrong, and the customer quietly works around it.
That’s why we analyze the actual conversation in context, rather than relying only on explicit complaints or predefined failure categories.
We’re still working on making this detection as reliable as possible, so this is a great area for us to keep improving. Thanks for putting it so clearly!
@inquio The thing I'd want before trusting it is your false positive rate on a hand labelled set. Subtly wrong answers are exactly where an LLM judge is confident and still wrong, so a grader that catches them is a real claim, not a feature. Publish that number, even if it's ugly, and I'd take this a lot more seriously than any accuracy line in the copy.