Score every conversation. Coach the ones that matter.

Quality assurance (QA) is the process of reviewing customer interactions to ensure agents meet standards — correct information, appropriate tone, policy compliance, resolution quality. Traditional QA involves managers manually reviewing samples. Dixa's Auto QA uses AI to score every conversation.

Auto QA uses AI to automatically review and score 100% of customer conversations against your criteria — not just random samples. It evaluates tone, accuracy, policy adherence, and resolution quality at scale, surfacing coaching opportunities and compliance issues that manual review would miss.

Manual QA typically covers 1-5% of conversations — managers physically review a small sample. Auto QA scores every conversation automatically, providing complete coverage. This catches more issues, removes selection bias, and frees managers to coach instead of review.

Auto QA can assess: greeting and closing quality, tone and empathy, policy compliance, accuracy of information, resolution completeness, response time, and custom criteria you define. Scores are consistent — no reviewer bias or mood variation.

Auto QA identifies specific conversations where agents struggled — not just a score, but examples. Managers can review flagged interactions, provide targeted feedback, and track improvement over time. Coaching becomes evidence-based, not anecdotal.

Yes. Define your own evaluation criteria, weight them by importance, and create different scorecards for different teams or conversation types. Your QA reflects your standards, not generic benchmarks.

Stuart saved 20 hours per week on QA reporting and saw 67% faster ticket review time. Instead of hours spent selecting and scoring conversations manually, managers focus on coaching and improvement — the actual value-add of QA.

Yes. QA scores flow into Dixa Discover dashboards alongside other metrics — handle time, CSAT, resolution rate. You see the complete picture of agent performance, not siloed data.