Production QA
A bad call should lead to a precise next change.
Build a repeatable Voice AI QA loop around production evidence, stable evaluation criteria, and regression checks that remain relevant as the agent evolves.
A practical operating loop
- Start with representative production conversations and the customer outcome that mattered.
- Evaluate a stable scorecard of business-critical behaviors.
- Investigate failed and uncertain cases with transcript, available audio, timing, tool events, and rationale evidence.
- Map repeated failures to the prompt, tool, policy, or workflow that owns the issue.
- Check both new calls and evaluation coverage after the change ships.
Keep humans in consequential decisions
Automated evaluation helps teams prioritize what to inspect. Human reviewers should validate uncertain, sensitive, or high-impact findings and calibrate the scorecard against representative calls before treating it as an operational gate.
Own your benchmark
Choose voice models using evidence from your own calls.
See how a private benchmark runs inside your environment, on your production scenarios, across the models you are considering.
30 minutes. Bring your model-selection question.