See why customers get frustrated — and coach the fix
Every conversation is analysed for sentiment, topics and friction, and summarized. Staff get a live copilot with suggested replies, quality and compliance are scored, and answers that aren't grounded in your knowledge are blocked before they reach a customer.
The problems it solves
If any of these sound familiar, this is for you.
You only hear about bad conversations when a customer complains.
AI can sound confident and still be wrong.
New agents take weeks to learn what good looks like.
How it works
Conversations are scored for sentiment and outcome as they happen.
Patterns that keep causing friction are surfaced with examples.
Suggested replies are checked against your knowledge before anyone sees them.
Use cases at a glance
Illustrative examples — the figures are made up to show what the outcome looks like, not real customer data.
What you get
- Live copilot: suggested replies and anger alerts for your team
- Recurring friction patterns, quality and compliance scoring, and coaching recommendations
- A guard that blocks answers not backed by your knowledge base
When you need it
You're ready for this when…
- You can't review every conversation by hand.
- You worry the AI might invent an answer.
- You want data to train and coach your team.
If two or more of these sound like your week, you'll see results in the first days.
Use cases
How businesses like yours put it to work.
- Support team
spots the delivery-delay wording that upsets customers most and fixes the script.
- Clinic
makes sure the AI only quotes real prices and opening hours.
- Call center
coaches new agents with real conversation examples.
Works well with
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