QA calibration

Quality assurance is the structured review of conversations against a scorecard, applied to human agents and AI agents alike. Traditional QA samples a handful of conversations per agent each month; automated QA scores every conversation, which changes the exercise from spot-checking into actual measurement.

A quality assurance score is the rating a customer service conversation receives after evaluation against a defined rubric, typically covering accuracy of information, process and compliance adherence, and communication quality such as tone, empathy and clarity. A simple rubric might weight ten criteria at 10 points each, with a call losing points for a missed disclosure or an inaccurate answer to land at 82/100. The score is only as reliable as the rubric’s clarity and the sample size behind it: manual QA programs conventionally review roughly 5 to 10% of conversations, so most interactions never get scored. OnClarity’s Agent QA scores 100% of conversations within five minutes, cutting QA operations cost by roughly 70%, and keeps scoring criteria visible so teams can calibrate evaluators against each other.

QA coverage is the percentage of customer service conversations reviewed and scored against a quality rubric. It measures how much of an operation’s actual customer interaction volume actually gets evaluated. Manual QA programs typically sample only a small slice of contacts. Industry data puts the standard at 2 to 5 calls per agent per month, often a low single-digit percentage of total volume. Everything outside that sample is a blind spot, where compliance breaches, coaching opportunities, and recurring issues go undetected. OnClarity’s AI Quality Agent scores 100% of conversations within about five minutes of call end, roughly 20 times typical manual coverage. That shift turns QA from a sampling problem into a question of rubric consistency and audit-trail completeness.

An agent scorecard rates a single customer interaction against fixed criteria. Categories typically include greeting and identity verification, accuracy of the resolution, compliance adherence, tone, and completion of required process steps. Each category carries a weight. Category scores combine into one percentage that feeds coaching sessions and QA reporting, and often rolls up into a team-level customer satisfaction index. Reviewed manually, scorecards cover only a small sample of conversations. OnClarity’s AI Agent QA applies a team’s existing scorecard rubric to every conversation across voice, chat, email, and WhatsApp.