We were running a customer-facing agent in production for about three months before we started using Plurai. Everything looked fine on the surface. Then we ran it through their evaluation pipeline and found a bunch of edge cases we never would have caught manually β responses that were technically correct but violated our policies in ways we hadn't fully defined yet.
That's what actually sold me. Not the benchmarks, though those are real. It was the realization that our previous "testing" was basically vibes. Plurai turned that into something measurable.
The thing I use most is the guardrail endpoint. Sub-100ms, fits into our existing stack without replacing anything. I was skeptical that a small custom model could outperform GPT-4-based judges but the accuracy on our specific use case is noticeably better β and cheaper by a lot.
Setup was surprisingly fast. I described what the agent should and shouldn't do in plain language, it generated boundary cases I hadn't thought of, and I had an endpoint to test against within the same day.
Plurai
Thank you!