A sales manager using UnleashX shared a moment that made our whole team smile.
A lead came in at 11pm from a company they d been trying to reach for months. Their AI employee replied within a minute, answered questions about pricing and integrations, found out they had a team of 140 and needed to go live before their quarter ended, and booked a call for 10am.
When the sales rep logged in the next morning, the handoff note was waiting: who the lead was, what they d asked, what they cared about most, and what was still open. The rep walked into the call already knowing everything. No so tell me about your company. Just a real conversation from the first minute.
They closed the deal that same week.
@istiakahmad That's a great question, Istiak.
At that scale, quality can't depend on manual reviews alone. AI-powered call evaluation can assess conversations against defined parameters like script adherence, accuracy, compliance, customer experience, and outcomes.
It also flags exceptions for human review, so teams can monitor quality consistently without listening to every call.
payment matching was the first thing we handed to agents, dull and easy to check. do your evaluations keep running after go-live, or only before launch?
@olia_nemirovski That's a smart place to start, Olia. 😄 And yes, evaluation shouldn't stop at launch. With UnleashX, agents can be monitored on an ongoing basis against defined parameters, with performance insights, quality checks, and exceptions flagged for review. The goal is to keep improving reliability as workflows run in the real world. What metrics do you prioritise for payment matching?
For escalation rules, I’d test calibrated abstention separately from task success. Build a set of deliberately ambiguous leads, policy conflicts, and missing-data cases, then measure whether the employee escalates to the correct owner with the missing evidence clearly stated. Otherwise a high completion rate can reward confident guessing.