QA coverage

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.

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.

A disposition code is the label an agent or AI applies to a closed contact to describe what actually happened. Disposition data drives volume reporting and root-cause analysis, so inconsistent or overly broad codes quietly destroy a support team’s ability to explain why contacts arrive at all.