Cekura Labs Workflow

Fix inaccurate AI voice agent evaluation metrics by identifying problematic calls, leaving structured feedback on misalignments, and triggering automated refinement to correct the metric prompts. This iterative cycle ensures higher reliability in evaluations.

An iterative metric improvement cycle is a structured workflow for diagnosing evaluation inaccuracies, providing actionable feedback, and automatically refining metric prompts to systematically progress from initial drafts to production-ready metrics.

Validate improved AI metrics by re-running the refined metric prompts on historical call data to confirm that the automated improvements correctly resolve previous misclassifications and align with expected quality standards.

Yes, you can use automated feedback loops to tune subjective AI quality metrics by accumulating detailed feedback on false positives or misalignments and leveraging it to automatically refine the metric logic.

Yes, the Cekura Labs Workflow supports systematic iteration from draft to production metrics by guiding users through a structured feedback loop to identify misalignments, leave feedback, and automatically improve metric prompts.