Solutions
AI sales coaching for sales managers
Coach the call that is happening, not the one from last quarter.
The short answer
Conversation intelligence tools coach retrospectively: a manager reviews a recording days later, when the deal has already moved. Axelize coaches inside the call, so the rep sees the suggested line while the buyer is still on the phone, and the manager shapes what gets suggested by curating the product and objection library.
What it handles
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Ramp new reps faster
A new hire has your best objection handling on screen from their first call, not after six weeks of shadowing.
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Make the playbook operational
Positioning lives in the product records the AI reads, so it shows up in calls instead of in a document nobody opens.
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See what actually gets said
Transcripts and suggestions per meeting, so coaching conversations start from evidence.
What we can actually show you
The numbers, and where each one comes from
Open the question mark on any of them for the measurement, the arithmetic or the paper it came from, including what it does not prove.
- +34%
- more work handled per hour by the least experienced people, once a live AI assistant arrives Where this number comes from Not our number, and not measured on Axelize. Brynjolfsson, Li and Raymond followed 5,179 support agents through the rollout of a real-time AI assistant. Issues resolved per hour rose 14% on average, 34% for the least experienced, and barely moved for the most experienced. It is support rather than sales, so treat it as the closest measured evidence that live help hands a newer person the habits of your best one, not as a promise about your pipeline. Brynjolfsson, Li & Raymond, Quarterly Journal of Economics, 2025
- One library
- of products and objections drives what every rep is told Where this number comes from Suggestions are built from the product records and objection material in the workspace, so curating that library is how a manager changes what the whole team hears. This is the practical difference from reviewing calls afterwards: the change lands on the next call rather than the next quarter.
- 1.4s
- median, from the buyer's last word to your rep's next line on screen Where this number comes from Measured end to end on our own calls: the caption lands, the model answers against the account context that was cached before you joined, the line renders beside the meeting. This is the median, not the best case. A long transcript or a slow network pushes the tail past three seconds, and when a request fails the credit is returned automatically, so a slow one never costs you anything.
The features behind it
- Account and contact context
One record, used by you and by the AI.
- AI that writes in your voice
Output you can read aloud, not notes you have to translate.
Frequently asked questions
- Can I control what the AI suggests?
- Yes. Suggestions are grounded in the products, positioning and notes your team maintains in Axelize, so editing those changes what reps see on calls.