Upload 30 real call traces. We return the gap between automated scores and human outcomes, with root-cause labels your team can ship against.
LLM-as-Judge agrees with human experts only 64-68% of the time. That gap is where your Agent's real failures hide.
Your Agent completed the task. But did it do it well?
Automated eval can mark completion. Human evaluation catches tone, intent, cultural context, instruction fidelity, and severity.Every audit turns production calls into a concrete operating view: score gaps, reviewed examples, failure taxonomy, and the smallest next fix.
We pair your automated score with calibrated human review, then show exactly where the model looked correct but failed the real user outcome.
Instruction misses, context loss, intent mismatch, tone failure, and escalation severity are labeled per call.
Reviewers are aligned on rubrics before scoring, with edge cases sampled for consistency.
Track whether a new prompt, model, or tool call improves the human outcome, not just the benchmark score.
Get the first readout within 24 hours, then a prioritized fix list before the next release window.
A clean operating model for teams that need credible human signal without slowing model and product velocity.
Send a compact batch of real user calls from production voice-agent flows.
We line up automated scores beside human verdicts to reveal the gap benchmark tests miss.
Reviewers score against the same rubric and flag boundary cases for consistency checks.
Your team gets root causes, examples, and a prioritized fix list for the next release.
For AI-native teams, human evaluation only matters if the process is calibrated, auditable, and fast enough to fit into release cycles.
Evaluators review anchor examples before scoring so pass, pending, and failure severity mean the same thing across the batch.
Ambiguous and high-impact calls are sampled for second review, creating a cleaner signal before the report ships.
Call batches are handled as evaluation inputs, with only the evidence needed for verdict and root-cause reporting.
First signal lands fast enough for launch decisions; deeper taxonomy follows with concrete examples and fixes.
Human evaluators caught what automated pipelines could not see in real customer interactions.
Get a compact verdict report with reviewed examples, root-cause labels, and the highest-leverage fixes for your next release.