Every conversation, evaluated
Stop double-listening to a 2% sample. Evaluation grids score 100% of conversations against your own criteria, humans and AI rate the same way, and the results turn into coaching.
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- Greeting and identification
- Needs correctly identified
- Upsell moment missed
Customers
AlloBrain customers include SNCF Connect, Club Med, Royal Commission for Riyadh City, Schneider Electric, Groupe ADP, FDJ United, Sofinco, La Poste, Intelcia, Crédit Agricole, Bank Albilad, GGVie, Leroy Merlin, Emma, BUT, RED by SFR, Génération, Outsourcia.
Evaluation grids
Multilevel, binary, or composite criteria. An overall score plus any extra scores you need: the opening of the call, compliance only, tone.
Calibrated with benchmark calls
Designate reference calls and check alignment between your ratings and the grid before a change ships. Everyone rates the same way, without ambiguity.
Coaching, not just scores
A page per agent, a co-pilot that writes the recommendation, and alerts when a score moves.
Evaluation grids
Score the way your best supervisor would
Criteria can lean on your knowledge bases and on call metadata (what Live Assist detected, the customer’s order history), not just what was said. Supervisors can adjust any score, with a required comment.
- Confidence level on every rating: when the AI doubts, a human decides
- Optimize rewrites ambiguous rules from reviewer comments
- Audit trail on every evaluation
Evaluation gridBilling · v4
CalibratedCriteria
Weight
- Greeting and identification10
- Needs correctly identified25
- Solution confirmed25
- Mandatory disclosure40
Agent space
A page per agent, visible to them and their team leader
Every exchange they handled, every evaluation, transcript and audio, and a discussion thread on a specific call. Agents can ask the co-pilot how they could have done better.
Agent pageSalma R.
Customer care- 128
- Evaluated
- 84.3
- Average
- +3.1
- vs last month
- Billing disputeToday · 6:1288
- Claim statusToday · 3:4874
- Address changeYesterday · 2:0591
Co-pilot
Ask why. Get the evidence and the coaching note.
Ask why billing calls score low and it returns the criteria that fail, the conversations behind them, and a recommendation you can give the team. Or one agent.
- Connects customer feedback to agent performance
- Summaries you can drop into a report
Co-pilotQuality
Why do billing calls score low?
Criteria that fail
Conversations
- Solution not confirmed38
- Mandatory disclosure skipped21
- Hold not announced14
Alerts
Weak signals, before they’re trends
Watch any score across any data source. Get the email, click through to the graphs, the verbatims, and the exact conversations that triggered it.
AlertQuality score, Billing
2h agoBelow 70 for three days running, against a team average of 84.
Daily quality score for billing calls over the last seven days: 82, 79, 81, 76, 68, 66, 64.
Compounding, not decaying
Quality monitoring trains coaching, coaching trains the AI agents
The same grid scores human and AI conversations. What fails on one side is what the other learns from. That’s the loop point solutions never close.
What teams see
- 100% of conversations evaluated, automatically
- Mandatory disclosures and scripts verified on every call
- Team leads review the calls that need it, not a random sample
- of calls evaluated
- 100%of calls evaluated
- what a manual sample typically covers
- 2%what a manual sample typically covers
- conversations analyzed daily
- 2M+conversations analyzed daily
- languages, including local dialects
- 180+languages, including local dialects
“The AlloBrain solution is highly effective, and we quickly saw the benefits for our business. It allows us to gain consistency, efficiency, and service quality, while keeping the human at the center of the relationship.”
- 98% of AI intent detections validated by experts
- Gold, Cas d'Or de l'Assurance 2026