Define what “good” sounds like
We transcribe a dozen top-performing human calls and annotate tone, pacing, and escalation triggers. Those examples turn into structured evaluation criteria that both the AI vendor and the client team can reference.
Every script change must map back to a target metric such as first-call resolution, compliance score, or conversion rate.
Tighten the human-in-the-loop
We route difficult intents to a human queue long before the AI is uncomfortable. This requires a router that can score sentiment and confidence in real time.
Supervisors review three to five anonymized calls per day with a checklist so feedback moves from anecdotes to documented improvements.
- →Flag every call that triggered a manual override and categorize the reason.
- →Summaries post automatically to Slack so product, compliance, and CX leads see the same feed.
Close the analytics loop
We treat transcripts as structured data. Calls feed into the same warehouse that powers RevOps dashboards so leaders can slice performance by intent, product line, or geography.
When the data shows drift, we retrain prompts with fresh examples and run A/B comparisons before rolling changes to all lines.