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.
Build with VaaniEval
Make evaluation coverage part of how your agent ships.
We are working with production Voice AI teams that need a quality system to keep pace with releases and real call behavior.