Case Quality Auditor You Can Trust: Comprehensive, Real-Time Auditing Across Every Closed Case

AI Case Quality Auditors are quickly becoming table stakes across support platforms. Many promise full coverage, automated scoring, and less manual effort—yet QA programs still struggle with low trust, low adoption, and limited impact on agent behavior.​

The issue isn’t AI.
It’s score-first QA.​

When QA stops at a number, agents don’t know what to improve, managers can’t prioritize coaching, and leaders can’t connect quality signals to outcomes like CSAT, escalations, and resolution quality.​

In this webinar, we unpack why generic AI Case QA often fails to scale—and what it takes to build comprehensive, real-time auditing that teams can trust. Instead of treating QA as a retrospective grading exercise on closed cases, we focus on an automated cadence that audits 100% of case volume as it happens and turns insights into timely, actionable feedback.​

You’ll see a signal-first approach that combines explainable analytics, positive path simulation, and near real-time feedback—so QA becomes a repeatable learning loop for agents and managers, not just a report.​

We’ll also cover what “trust-ready” QA looks like in practice, with explainability, governance, and model evaluation built in—so teams rely on QA signals to guide decisions, not just track metrics.​