AI Quality Assurance

It scores conversations against your own QA guidelines and knowledge base, then provides a clear reason for every score so reviewers understand the result. Because the scoring rules are consistent across all conversations, the AI removes reviewer bias and applies the same standard each time. You set the review range, choosing some or all of your chats.

AI QA can review every customer interaction rather than the small sample most manual programs check. Scoring all conversations gives a complete picture of support quality, makes SLA monitoring more accurate, and lowers QA costs by automating work that reviewers would otherwise do by hand. You still control the scope and can narrow the range when you want.

Yes. Managers can review and override AI assessments before results are shared, so the final evaluation reflects human judgment where it matters. This keeps QA accountable to your team while the AI handles the volume, and it builds reviewer confidence because every score comes with a stated reason.

Each QA result converts into a coaching opportunity with a single click. Managers add comments and tags, then assign follow-ups to the right people, and agents view that feedback directly in the Agent Console in read-only form. Connecting scoring to coaching this way shortens the path from a flagged conversation to an actual performance conversation.