1
Add AI Evaluated Conditions
Custom qualitative criteria evaluated by AI based on call transcripts and context.Name — A short identifier (e.g., “Call resolved”, “Customer satisfied”, “Issue escalated properly”).Prompt Description — The prompt the AI uses to evaluate whether the condition was met.Example:
"AI agent was able to resolve user's query"Best practices:- Be specific about what success looks like
- Include relevant context about the call type or use case
- Use clear, unambiguous language
2
Add Performance Metrics
Quantitative thresholds calls must meet to be considered successful.
Click + Add to add multiple metrics.
A call is considered successful only if it meets all defined criteria across both AI Evaluated Conditions and Performance Metrics.
3
Configure Weighted Scoring (optional)
Enable Weighted Scoring to assign different weights to your criteria — giving more importance to certain conditions or metrics.When enabled: Assign weights to each condition and metric, then set a Success Criteria threshold.When disabled: All criteria are treated equally — a call must meet all conditions.Use weighted scoring when some criteria are more important than others. For example, weight “Call resolved” higher than “Customer satisfaction” if resolution is your primary goal.
4
Save and Run QA
Click Save and Run QA to finalize and start analysis.If you encounter a validation error, review all conditions and metrics to ensure required fields are filled and thresholds are set.