Confidence scoring (AI evaluation) is a score or probability attached to an AI-generated judgment about a call, such as whether required disclosures were read, identity verification was completed, or a prohibited phrase was used. It reflects the model’s certainty given the audio quality, transcript clarity, and how closely the conversation matches patterns it has learned.
Operationally, confidence scores help contact centers manage risk and workload by setting review thresholds. High-confidence results can be used for routine monitoring and trend reporting, while low-confidence results can be routed to QA for verification, reducing false positives/negatives that can distort compliance metrics.
Confidence scoring also supports audit readiness by documenting how strongly the system supported each finding and by enabling consistent rules for escalation. Tracking confidence over time can highlight issues like poor audio capture, new scripts, or changing agent behavior that reduce evaluation reliability.
