Use case · quality control
Script and standards control on every conversation
Service standards only work when they're followed on every contact, not on the 2–3% a supervisor manages to review. The platform breaks down every interaction against your stage checklist — greeting, needs discovery, presentation, objection handling, close — and turns script adherence into a measurable metric per agent, with alerts on breaches.
The problem
The company has a script and standards, but checking adherence at scale by hand is impossible. A supervisor listens to a few-percent sample, and the rest of the conversations stay a black box: that an agent skipped the greeting, didn't uncover the need, or "forgot" to handle an objection surfaces by chance or through a customer complaint.
Sampling is also subjective: two reviewers grade the same dialogue differently, there's no shared criterion, and the agent has nothing to argue with or hold onto to improve. Standards become a poster on the wall rather than what actually happens on the line.
Both conversion and customer experience suffer: a script breach is seen after the fact, a trend like "needs discovery has dropped on the second shift" is noticed months later, and systemic gaps only show up when sales fall or complaints rise.
How the platform solves it
- 1
Describe your stage checklist
Standard greeting, needs discovery, presentation, objection handling, close — phrase the items in plain words. AI rule creation sets up the filters and check logic to match your standard.
- 2
An automation rule checks every conversation
Every new recording triggers an automation: from the transcript, AI grades adherence for each stage and fills metrics (pass/fail per item, an overall adherence percentage) via an llmText or script action.
- 3
AI tags breaches by meaning
A tagLlm action applies tags by meaning: "greeting skipped," "objection not handled," "no close." Tags are the same filter you use to pull the set of problem calls in one click.
- 4
Adherence metrics per agent on a dashboard
Script adherence percentage and a per-stage breakdown roll up into charts: comparison across agents, shifts and periods, and a trend for each checklist item. You can see exactly where the standard is slipping.
- 5
Alerts and exports on breaches
For critical breaches the automation sends an email or a signed webhook, and the matched contact with a note goes to your CRM. The supervisor gets a precise list of calls to review, not "listen to everything."
The result
Standards adherence turns from a subjective sample into an objective metric across 100% of conversations: you can see every agent, every stage and every breach — at the moment it can still be fixed.
- The checklist is verified on 100% of conversations, not a few-percent sample.
- A single criterion: AI grades every dialogue the same way, giving the agent something to rely on.
- Adherence metrics per agent, stage and shift — trends show up immediately, not months later.
- Breach alerts inside the real loop: you review targeted calls instead of listening back-to-back.
Key facts
- Coverage
- The stage checklist is verified on 100% of conversations automatically, not on a sample.
- How to set up
- Describe the standard's items in plain words — AI rule creation assembles the automation to match your checklist.
- Metrics
- Script adherence percentage and a per-stage breakdown — custom metrics that sync into analytics and surface on dashboards.
- Alerts
- Breaches go to email, a signed webhook, or a note in your CRM against the matched contact.
- Channels
- The same logic applies to written channels: adherence to response standards in chats, email and tickets is checked the same way.
FAQ
Frequently asked questions
How does AI decide a script stage was met?
You describe the stage checklist in plain words, and an automation runs an AI action over the transcript on every recording: it grades each item and fills a metric — whether the stage was met and the overall adherence percentage. You can attach a reference document with the standard as context.
Can I check every agent, not a sample?
Yes. The automation rule fires on every new recording and can be run retroactively across the archive, so the checklist is verified on 100% of conversations. Sample listening is replaced by an objective metric per agent.
How do I learn about a breach in time?
The automation tags breaches by meaning and, for critical cases, sends an email or a signed webhook, while the contact matched by number gets a note in your CRM. Instead of "listen to everything," the supervisor gets a precise list of calls to review.
Does this work only for calls?
No. The same loop applies to written channels — chats, email and tickets: response standards are checked against a checklist just like conversation stages, with the same metrics, tags and alerts.
Product
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