Plan requirement
| Subscription | Any plan |
| Also required | Quality Assurance or Workforce Engagement Management |
| Access | Agent |
It reads the text of the conversation and scores observable features. What that means in practice.
What it works from
The words in the conversation: what the customer wrote, what the agent replied, and the order they came in. Each enabled category is applied to that text.
What it can see
- Whether a greeting and closing appear.
- Spelling and grammar.
- Words and phrasing associated with empathy or apology.
- Whether the agent's reply refers to what the customer described.
What it cannot see
- Whether the answer was correct.
- A phone call, an internal note, or advice from a colleague.
- Why an agent deliberately skipped a step.
- Anything in an attachment.
It scores everything, not a sample
Unlike manual reviewing, every conversation in scope is scored. That is its main advantage: patterns across hundreds of conversations become visible.
Its scores are an input
They belong in front of a reviewer deciding what to look at, not in front of an agent as feedback. A machine score with no human comment is not something anybody learns from.
Where it disagrees with you
Read the conversation. If you think the agent did the right thing and the score says otherwise, the category is wrong for your context, and turning it off is a legitimate answer.
Language affects it
Performance differs between languages. Compare within a language rather than across, or the difference you see may be the tooling.
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