Review where it helps.
Use sampling rules and review rounds to select conversations. Check AI drafts against the source and answer questions about the score.
Quality at scaleHigh standards.
High standards.
As standard.
Find the conversations worth checking, coach from the evidence, and assign recurring problems a fix. Keep customer outcomes separate from review scores.
More conversations.
The same care.
A shared rubric gives every reviewer a common starting point. The conversation behind each score keeps the feedback specific, wherever it’s needed.
Conversation review
92/100
Same rubric. People and AI.
Accuracy96
Empathy & tone94
Clear next steps86
Make the next step clear.
“I’ll send you an update by Friday.”
Product preview with example data.
Turn the moment into coaching.
Carry examples and commitments into a coaching session. Calibrate judgments together and version changes to the standard.
Fix the pattern.
Give recurring causes an owner on the Tracker. Record what was tried and follow measured recontacts with the lookback and provisional window visible.
Questions,
answered.
Can we automate every judgment?
AI drafts the review; people check and decide. Sampling, calibration, and disputes help assess the draft rather than treat agreement as proof.
Are review scores used to rank employees?
No. The current Quality experience uses reviews for coaching and improvement. Customer outcomes are measured separately and also are not people rankings.
How do we know a change helped?
Record the fix and inspect subsequent customer outcomes. Coverage, settlement time, and comparison limits determine what the evidence can establish.