We run the evaluation your pipeline skipped — measuring real human preference across every output your Agent produces.
LLM-as-Judge agrees with human experts only 64–68% of the time. That gap is where your Agent's real failures hide — and where automated eval stops being useful.
Certified local evaluators assessing your Agent the same way real users will judge it.
Pinpoint where your Agent fails and why — model, harness, or context. Human evaluators run Outcome + Transcript dual-track assessment across voice, video, and text.
Catch regressions before your users do. Continuous monitoring against real-world performance — not synthetic benchmarks.
Native-speaker evaluators across 17 markets — linguistic depth and cultural fluency that automated pipelines can't replicate.
Evaluators who understand your domain's terminology, norms, and expectations — not just whether the task was completed.
Early signals fast, full diagnostic to follow — act before your next release, not after it ships.
We work with you to define the evaluation dimensions and failure taxonomy — what good looks like, and what counts as a miss.
AI evaluates your Agent traces at scale — scoring outcomes, flagging ambiguous cases, and building a structured picture of where performance breaks down.
Certified evaluators handle the boundary cases and specialist decisions — the calls that require context, domain expertise, or cultural fluency.
Every failure classified by root cause: Model, Harness, or Context. You get a prioritized action plan — not just a score.
Human evaluators caught what automated pipelines couldn't.
Free diagnostic. No commitment. See exactly what your automated eval is missing.