AI Coaching
Future Ready turns the call that scored low into a voice role-play your agent runs out loud, as often as it takes.
Persona, scenario and rubric come from the conversation that scored low.
The agent talks, and the AI answers, objects and interrupts in real time.
The skill is scored again on the calls after the drill.
Kari Nilsen
Wants to cancel · frustrated about a doubled bill
Kari is speaking
YouKariMy bill’s doubled and no one’s replied. I want to cancel.
82/100
EmpathyAI-scored just now
Re-measured on real calls
A Norwegian call, translated here, scored on your rubric with the sentence behind every score, then turned into a practice drill.
Every call heard
Hi, you’re speaking to Jonas at Nordlys Energi.
My electricity bill is twice what it was last year, and no one’s replied to my email.
Yes, prices have gone up for everyone.Empathy
Then I’m thinking of switching suppliers.
Scored, with the evidence
Scorecard
Greeting
MetEmpathy
Not metAI rationale: Answered a doubled bill with a general price fact, without naming the customer’s frustration.
Yes, prices have gone up for everyone.
Agent · JonasResolution
Partly metAI-scored 14 Oct 10:48Every score can be disputed
Coached on the gap
Practice built from this call
Name her worry before the price
Coaching tipTry: “I can see why that bill came as a shock. Let’s look at it together.”
Proven on the next calls
Example · Empathy, Jonas’s 30 calls before and after the drill
Re-measured on real calls, not on the drill.
Every contact centre knows what good looks like. Almost none can give an agent enough repetitions to get there: a team leader sits in on a handful of calls a month, and a training day happens twice a year. What's missing is saying the difficult sentence out loud, again and again, until it stops being difficult.
A role-play only works if the other side is difficult. The customer persona holds its position, interrupts, repeats an objection that hasn't been answered, and gets impatient when the agent hedges. Difficulty adapts as the session runs: handle it well and it escalates, struggle and it eases off rather than turning practice into a failure. The agent speaks out loud and hears themselves doing it.
Practice that depends on a manager's calendar is practice that mostly doesn't happen. A drill runs at 21:00 on a Tuesday, in the twenty minutes before a shift, or three times in a row straight after a call that went badly. New agents ramp on their own schedule instead of waiting for the next cohort, and none of it consumes supervisor time to make possible.
Most training spend is defended with a completion rate, which measures attendance rather than change. Because the same platform scores real conversations, the gain is measured where it counts: on the calls that come after the drill, against a baseline of peers who didn't run it. If a skill didn't move, you find out, and the loop proposes the next drill rather than repeating the last one.
No. Trainers set the standard and make the judgement calls. The AI handles the repetition, which is the part that doesn't scale and the part agents need most.
They speak. A drill runs as a live voice conversation in the browser: the AI plays the customer, the agent answers out loud, and coaching cues appear during and after the session. A typical drill takes a few minutes.
Yes. The AI customer speaks the language your team actually sells and serves in, and the drill is scored in that language rather than translated into English first.
From their own scored calls. The criterion that scored lowest becomes the next drill, and a gap that keeps recurring proposes its own follow-up without a manager having to notice it first.
You can, and some teams start there with scenarios they write themselves. It's considerably stronger with scoring attached, because then the drills come from real calls and the gain can be measured instead of assumed.
Book a 30-minute call. Bring one conversation that went badly and we'll turn it into a role-play while you watch.
