We Don't Think Chatbot Evaluation Is About Ground Truth

When we started building Inquio, we kept coming back to one question: How do you actually evaluate a chatbot at scale?

The obvious answer is to compare its answers against a ground-truth dataset. But that doesn't work for many real-world conversations. If a chatbot tells a customer that a savings account pays 5% interest, You can't simply declare that answer wrong unless we have a reliable source telling us the correct rate is 4.5%. And maintaining ground truth for every possible conversation quickly becomes impractical.

So we took a different approach. We look at the conversations themselves.

When you analyze a large volume of real interactions, patterns start to emerge: - Users repeatedly correct the chatbot. - The same questions lead to frustration. - Certain answers trigger follow-up questions or abandonment. - Similar conversations fail in similar ways. - Issues that look like isolated incidents turn out to be systematic.

The signal isn't necessarily in one conversation. It's in the patterns across thousands of them. That's why Inquio automatically analyzes conversations, clusters them by issue, and helps identify which problems deserve attention first. We help teams discover where their chatbot is consistently failing users — and what to fix next.

That's our point of view on chatbot evaluation: Don't just ask whether an answer matches a test set. Look at what actually happens when real users talk to the bot.