Support QA scoring on every conversation, not a small sample

Manual QA reviews a small sample of conversations, so most coaching rests on luck. Scoring every conversation against your own rubric, with the quote behind each score, turns QA into a full picture and leaves reviewers free to calibrate and coach.

01the problem and who owns it

QA analysts listen to a handful of calls per agent per month and fill in a scorecard. Agents see the score weeks later, often for a conversation they no longer remember, and argue the sample was unrepresentative. Compliance-critical steps, like identity verification or required disclosures, may never be checked on most calls.

The QA lead owns the rubric and team leads own coaching. Both need coverage and evidence more than they need another dashboard.

02what the AI does, step by step

1. Capture conversationsCall recordings are transcribed with speaker labels; chat and email threads are pulled as text. Each conversation is linked to the agent, queue, and customer outcome such as resolution or satisfaction rating.
2. Turn the rubric into checksEach rubric item is rewritten as a specific, observable question, for example whether the agent confirmed the issue was resolved before closing. Vague items like professionalism are broken into concrete behaviors.
3. Score with quoted evidenceThe model answers each check and quotes the transcript lines that support the answer, or marks the check not applicable. Scores without evidence are not accepted.
4. Flag compliance missesRequired steps such as identity verification, recording disclosures, or regulated scripts are scored strictly and missed steps are flagged to the QA lead within a day.
5. Calibrate against humansA weekly sample is scored by both QA analysts and the model. Disagreements are discussed and used to sharpen the rubric wording and examples.
6. Deliver coaching viewsTeam leads see trends per agent and per rubric item, with links to example conversations, so coaching sessions use specific moments rather than averages.

03systems it connects to

• Contact center platform. Talkdesk, Five9, Aircall, Zoom Contact Center, or similar for recordings.
• Transcription. A speech-to-text service with speaker separation.
• Helpdesk. For chat and email transcripts and outcomes.
• QA workspace. Where scores, evidence, disputes, and coaching notes live.

04human checkpoints

• Rubric ownership. The QA lead approves every check and its wording; the model does not change the rubric.
• Disputes. Agents can dispute any score, and a person resolves it.
• Consequential decisions. Performance reviews and discipline never rest on model scores alone; a person reviews the underlying conversations.

05what to measure

• Coverage. Share of conversations scored.
• Human agreement. Agreement between model and calibrated reviewers, per rubric item.
• Compliance miss rate. Missed required steps per team over time.
• Outcome correlation. Whether rubric items actually relate to resolution and satisfaction in your data.

06risks and guardrails

• Recording consent. Call recording and analysis rules vary by state and country, and some require all-party consent. Confirm disclosures with counsel before analyzing calls.
• Employee trust. Agents should know how scoring works and how it is used. Secret scoring damages morale and may raise employment law questions.
• Sensitive data in transcripts. Calls contain card numbers, health information, and identity details. Redact before storage and analysis, and keep PCI and HIPAA scope in mind.

07build vs buy

Conversation intelligence and QA products like MaestroQA, Klaus (now part of Zendesk), and contact center suites offer automated scoring. For standard rubrics, evaluate those first.

Build when your rubric is specialized, regulated scripts must be checked precisely, or you need scoring joined to outcomes in systems those products do not reach.

08related playbooks

Browse every customer support playbook or the full library.