Quality assurance score

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.

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. Clarity’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 evaluation set is a fixed sample of real conversations used to score an AI agent’s quality before and after changes. Holding the sample constant is what makes results comparable, so evaluation sets are versioned and refreshed only deliberately when the underlying contact mix genuinely shifts.

Interaction analytics is the analysis of full conversation transcripts to surface demand drivers, friction, and risk across every contact rather than a sampled few. Because it reads what customers actually said, it identifies emerging issues that structured ticket fields and survey scores are too coarse to reveal.