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AI QA automatically evaluates a sampled set of your calls against rules and metrics you configure. It surfaces high-level trends — average score, resolution rate, latency — and call-level diagnostics like hallucinations, knowledge base accuracy, overlapping speech, sentiment, and tool usage, each backed by transcript evidence.
Call QA Overview dashboard showing calls analyzed, average score of 87, call resolution rate of 75%, transfer success rate, transfer wait time, and a top questions from users table with resolution rates.

The Call QA Overview shows summary metrics and trends across a cohort of analyzed calls.

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Use AI QA to

  • Track call quality and resolution over time
  • Identify failure patterns and their root causes
  • Review individual calls with transcript-level evidence
For example, a health clinic runs AI QA on its appointment-booking agent to catch when the agent gives wrong hours or mishears a caller’s name, then uses the top questions view to see which caller intents resolve least often.

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How it works

1

Create a cohort

Group the calls you want to evaluate by agent, date range, and other filters, and set how many to sample. See Define a QA cohort.
2

Define resolution criteria

Set the AI conditions and metric thresholds that decide whether a call counts as successful. See Define resolution criteria.
3

Review results

Read aggregate trends, drill into individual calls, and act on flagged issues. See View QA results and Address metric issues.
New here? Start with Access AI QA. For definitions of every metric and term, see AI QA metrics.

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Pricing

AI QA is free for the first 100 minutes of analyzed call time per workspace. After that, it’s priced at $0.10 per minute of analyzed call time.