Firetone treats a call as three moments where a decision can be improved: who to dial and when, what to say while the line is open, and what the organisation learns once it closes.
A per-record probability of a live answer, built from call history, time of day, number type and campaign context.
Contact-level reachability windows that reorder the list instead of grinding through it top to bottom.
Route the records worth a senior agent to a senior agent.
Live transcription with the next-best action, the objection answer and the required wording on screen.
Natural-speech intent capture that completes routine requests and escalates with full context.
Escalation alerts to a supervisor while the call can still be saved.
Your rubric applied to 100% of calls, with the evidence timestamped in the transcript.
Missed disclosures, prohibited phrases and consent gaps flagged per call and per agent.
Disposition, summary and follow-up task written to the customer record automatically.
AI decisions in a regulated contact centre have to be inspectable. Firetone shows the reason behind every score and keeps a human able to override it.
Each prediction lists the factors that drove it, in the console and the export.
Recordings and transcripts are tenant-isolated and are not used to train shared models.
Pacing, routing and bot handoff thresholds are all manual controls you can pin.
Illustrative view of the score explanation panel in the Firetone console.
Most contact centre AI is a reporting layer. Calls happen, recordings pile up, a model reads them later. Useful for trends, incapable of changing any call it analyses.
Firetone's models sit in the path. Prediction runs before dialing and changes who is dialed. Assist runs mid-call and changes what the agent says. Compliance flags a miss the day it happens.
A per-record probability of a live answer, used to decide dialing order.
Contact-level reachability windows that re-order the list.
Routes the records worth a senior agent to one.
Live transcription with next-best action, objection responses and required wording on screen mid-conversation.
Natural-speech understanding that completes routine requests and escalates the rest with context attached.
Escalation alerts to a supervisor while the call is still open and still recoverable.
A decision you cannot explain is a decision you cannot defend. Someone may have to account for why one account holder was called at seven and another was not called at all.
So every score lists the factors behind it, in the console and the export. A 0.74 breaks into its contributions. Supervisors can disagree, override, and pin thresholds by hand.
Traditional quality assurance is a sampling exercise, and everything about it is shaped by that constraint. A reviewer listens to a handful of calls per agent per month because listening is slow. Coaching is therefore retrospective and thin. Systemic problems stay invisible until they are large. An agent's score depends substantially on which calls happened to be pulled.
Scoring all of them changes the questions that can be asked. Not 'how did this agent do on four calls' but 'which disclosure is missed most often across the floor, is it missed more on long calls, and did it start after the script change three weeks ago'. Those are process questions with process answers, and they are invisible at a two percent sample.
No. Recordings and transcripts are tenant-isolated and are not used to train shared models, in any deployment mode.
Yes, and this is deliberate rather than incidental. Every prediction lists the factors that produced it, in both the console and the export.
Live transcription of the conversation, the next-best action or offer given what has been said, responses to the objection just raised, and any wording your scripts require at that point — for example a mandated affordability line before an arrangement is confirmed. It also shows live scores such as promise-to-pay likelihood and script compliance as they update.
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