Caller gave medication detail, but the note only said “needs callback.”
Queue AI flags the incomplete handoff and keeps the example attached to the review.
AI QA
Score every call, not random samples.
Queue AI reviews the operational details TAS leaders care about: greeting quality, dispatch accuracy, note completeness, client-script adherence, and whether the handoff was clean enough for the next person.
Today's QA
92%
Greeting 98%
Dispatch accuracy 88%
Notes quality 91%
Client script 94%
Follow-up language 86%
Real QA moments
AI QA should recognize the messy calls supervisors already worry about.
Review in context
Every score should point to a real call, not an abstract metric.
Agent skipped the equipment question during a no-heat call.
The score shows the missed script step without making a supervisor hunt for the recording.
Caller reported water intrusion, but urgency language was missed.
QA surfaces the account-specific miss while the call is still coachable.
What supervisors get
Less sampling, fewer surprises, better coaching material.
The point is not to replace judgment. It is to make sure supervisors spend their judgment on the right calls.
01
Filter by client, shift, agent, or queue event
02
See which QA dimensions are slipping first
03
Link scores back to recordings and coaching tasks
04
Track account-level quality patterns