← Back to Success Stories
Enterprise Call Center
Confidential Client
Contact Center
Custom Models
We built a real-time voice analysis system that gives actionable coaching feedback to call center trainees—so managers can scale training without scaling headcount.
$150K
annual savings on training resources
8%
productivity increase
100+
agents per manager
The client
A large enterprise financial institution that operates call centers handling millions of customer interactions annually. Due to the nature of call center work, they experience high employee turnover—agents leave on average once a year—requiring constant onboarding and training of new staff.
The challenge
- The client had built a simulated training system that guides new agents through various call scenarios on-screen.
- Managers still needed to listen in on training sessions to assess how well trainees were performing and provide coaching.
- With each manager overseeing 100+ agents and constant turnover, there simply weren't enough managers to monitor all trainees effectively.
- Training quality was bottlenecked by management capacity, not trainee availability.
What we shipped
- Custom-trained voice analysis model built on MFCC (Mel-frequency cepstral coefficients), spectrograms, and CNN architecture to detect vocal patterns in real-time
- On-premise GPU deployment to meet enterprise security requirements and deliver near-real-time feedback with sub-second latency
- Actionable, real-time feedback on specific vocal attributes: pace (slow down/speed up), pitch modulation, clarity of pronunciation, vowel articulation
- Word choice monitoring that flags problematic phrases and suggests alternatives
- Integration with existing training simulation platform for seamless trainee experience
- Manager dashboard showing trainee progress and areas needing human intervention
Results
- $150K annual savings on hiring dedicated training resources
- 8% productivity increase year-over-year across the call center operation
- Managers handle constant trainee influx without sacrificing their core management responsibilities
- Faster time-to-competency for new agents with immediate, consistent feedback
- Scalable training capacity that grows with hiring without proportional management overhead
Why it worked
- Real-time feedback loop: trainees correct behavior immediately, not days later in a review session
- Specific, actionable guidance: "slow down" and "raise pitch" are behaviors agents can change on the spot
- Augmented managers, not replaced: AI handles volume; humans handle exceptions and complex coaching
- Integrated into existing workflow: built on top of their simulation platform, not a separate system