How Quality Review Works in BearScope
Inspect available conversation scores, review the rubric, and turn a finding into coaching or customer recovery.
A quality score earns its place when it helps someone make a better decision: check a conversation, coach a specific behavior, or help a customer who still needs attention.
BearScope’s Quality workspace brings those steps together. AI scoring coverage depends on the connected channels, available transcripts, active rubric, and scoring configuration. Check the scored count against the eligible conversation count before describing coverage as complete.
The rubric defines the standard
Rubric criteria describe observable behavior: whether the issue was understood, whether the answer was correct, and whether the next step was clear. BearScope supports pass/fail criteria grouped into weighted categories, with critical criteria where applicable.
Authorized reviewers can inspect rubric instructions and manage versions. A new active version applies to future scoring runs; it does not silently rewrite historical scores. Some criteria may need manual review rather than AI scoring.
Review the evidence behind a score
Open an available scored conversation and read the exchange alongside its criteria. Transcript evidence can help explain a mark, but the reviewer still needs to judge whether it supports the conclusion. An unavailable transcript or missing source limits what can be established.
A score is a first assessment. A reviewer can confirm or correct individual criteria and add the context needed for the rep. Disputes give contested scores a path to review rather than leaving a number unexplained.
| Review question | What to inspect |
|---|---|
| Was this conversation eligible for scoring? | Channel, transcript availability, scoring configuration |
| What standard applied? | Active rubric and version |
| Does the score fit the exchange? | Criteria and available conversation evidence |
| What happens next? | Review decision, coaching commitment, or customer follow-up |
Calibrate before drawing conclusions
Calibration helps reviewers compare their judgments on the same material. Use disagreement to find unclear criteria, missing context, or an AI assessment that needs correction. A correction does not by itself establish that the scoring model automatically learns from it.
Team scorecards help show strengths and areas for attention across the available scored work. Read those trends with their coverage and time window; a partial channel can give a partial picture.
Carry the finding into improvement
Coaching turns an observation into a specific commitment that the team can revisit. Customer recovery has a different job: find customers waiting after AI handoffs, check possible missed promises, and organize the follow-up they need.
Recurring problems can become owned fixes in the Tracker. Available customer outcomes help the team check whether the work improved service, rather than counting completed reviews alone.
Book a demo to inspect one scored conversation, its rubric and coverage, and the path from a review finding to coaching or customer recovery.
See the work in BearScope.
Choose a workflow. We’ll demonstrate the current views, review your sources, and agree what a pilot should measure.
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