Auto QA conversations grade 100% of automated customer service conversations according to quality standards. AI reads tone, information accuracy, and policy compliance, then scores and immediately warns of poor conversations. The QC team no longer reads samples manually, nor does it miss anything. Zenify recorded 98.4% accuracy in AI processing.
TL;DR
- Auto QA scores 100% of conversations, not just samples.
- Criteria: tone, information, policy.
- Immediate poor dialogue warning.
- QC focuses on deep work, AI takes care of even parts.
- Turn QC from "after the event" to "right after the event".
What is Auto QA conversation?
Auto QA uses AI to self-assess conversation quality according to the following criteria:
Criteria | What dot
Tone | Polite, sympathetic, not irritable
Information | Price, policy, product right
Policy | No promises beyond scope
Resolve | Successfully answer customer problems
Process | Stick to the standard script
How is it different from manual QA?
Criteria | Manual QC | Auto QA
Scope | 5-10% sample | 100%
Detection speed | After day/week | Immediately
Uniformity | Depends on the judge | A unified standard
Cost | Large QC team | Automation
Exception | Flexible handling | Need a tester
Why is auto QA necessary?
1. Discovering errors too late = losing customers
Grumpy conversation, incorrect policy that QC read after 1 week → customer left.
Auto QA alerts when happening (see AI QA).
2. Sample is not representative
Read the sample 10%, the remaining 90% of risks no one sees.
Score 100% to know the real situation.
3. Score according to a standard
3 QCs grade 3 types, auto QA grades one standard.
Fairness for employees, standard data for management.
4. Provide training data
Conversation with poor score → right topic that needs training (see Agent skills).
How to implement Auto QA
Standard definition: scale, criteria, weight according to your industry.
Sample labeling: let AI learn from good/bad conversations.
Automatic marking: runs on the entire conversation.
Immediate warning: score below threshold → management notification.
Regular review: people check the sample so that the AI does not deviate from the standard.
Note when using Auto QA
AI scores according to the standards you set: sketchy standards → sketchy results.
Combining people: AI does not completely replace QC, QC works deeply + exceptions.
Explanation of points: Employees need to know why they lost points to correct it.