Our cases
AI Call Evaluation for Customer Service Quality
Problem
For companies with large customer support teams, ensuring consistent service quality is a constant challenge. Traditional manual review of calls is slow, subjective, and expensive. Critical issues like weak empathy, refusal to help, or unprofessional greetings are often ignored. These gaps reduce customer satisfaction and damage brand trust. Our client needed a scalable and objective way to evaluate thousands of calls quickly and reliably.
Result
By automating call evaluation with AI, the company gained a consistent and unbiased quality monitoring system. Every call could be transcribed, analyzed against a defined checklist and flagged for rule violations within seconds. The result was faster detection of service quality issues, more effective coaching of employees, and higher customer satisfaction. Managers now have clear, data-driven insights instead of relying on limited manual reviews.
How did we achieve it?
We built a pipeline that records and transcribes each call using speech-to-text neural networks. The transcriptions are uploaded to a server, where advanced language models analyze them against a checklist of common rule violations. Model parameters (temperature, prompts, network selection, etc.) were carefully tuned for accuracy. Each analysis produces a structured JSON report, stored in a database and categorized by violation type. Dashboards and reporting tools provide managers with actionable insights to guide training and improve service quality at scale.
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