Train for the call before the call happens.

The first minute of an emergency call decides a great deal: whether the location is confirmed, whether the true nature of the emergency is understood, and whether the caller becomes a partner or an obstacle. Those skills are hard to build from a manual, and live calls are the wrong place to learn them.

ctReadySim^™ places call takers in realistic emergency calls with a dynamic, AI-performed caller. The trainee answers on a headset, speaks naturally, and works the call to completion — obtaining the location, questioning effectively, controlling the conversation, and delivering instructions under time pressure.

Calls that push back

The caller is not reading from a script. A panicked mother may talk over the call taker until she is reassured in plain, confident language. An intoxicated caller may give a wrong address before correcting it. A whispering caller may go silent when asked the wrong question. What the trainee says — and when — changes what happens next.

Scenarios can also change mid-call: a patient stops breathing, a prowler moves closer, a caller hangs up and must be called back. The scenario engine controls those events and the facts behind them; the caller performs the human reaction.

How performance is measured

After each call, the evaluation ties results to evidence: when the address was verified, which required questions were asked, how quickly life-safety instructions began, and how the caller’s cooperation changed as the call was handled. Trainers review recordings, transcripts, and scores — and can override any result with an audit trail.

Emergency-communications scenarios exist in the platform today, including protocol-guided (guidecard) training modes. Agencies can also author scenarios around their own protocols and standards.