The reported metric has a clear boundary
The public account describes roughly 260 paid users and EUR15,000 MRR after seven months. It is a team member's self-report, not audited profit, take-home pay, or a promise for another student.
The product also depends on sales knowledge, customer access, and the ability to support a recurring training workflow. Those conditions should stay visible.
Package practice, not a chatbot
An AI sales trainer becomes concrete when it gives a representative a role-play, scores a call, and suggests a next practice step. Define the scenario, rubric, and review loop before choosing a model.
A small team can test one sales motion with a fixed set of redacted examples. The buyer should be able to tell whether the practice changed behavior or merely produced fluent feedback.
Track adoption and repair
Count completed sessions, failed requests, manual review, support time, and renewal behavior. Paid seats without continued use are not the same as durable value.
Write a stopping rule for the pilot. If the team cannot identify a repeatable improvement, revise the workflow before adding more scenarios.
Where APIToken fits
APIToken can host a bounded comparison of currently available models with an isolated key, usage records, status checks, and a budget ceiling.
The source provides no evidence of APIToken use. The EUR15,000 figure remains a self-reported MRR metric and should not be rewritten as personal income. Review renewals and completed practice sessions before treating the result as durable evidence.