Call Center Operation

The Challenge

A call center operation handling inbound and outbound calls for multiple clients had no systematic way to measure call quality. QA was manual — supervisors would listen to a random sample of calls and score them on a subjective rubric. Agents received inconsistent feedback, customer sentiment was unmeasured, and first-call resolution rates were below industry benchmarks. The operation was scaling, but quality was not scaling with it.

Our Approach

We proposed a three-layer intelligence system: real-time call scripting assistance, post-call sentiment analysis, and automated QA scoring. The approach was designed to improve agent performance in the moment (scripting), generate actionable data after every call (sentiment), and replace subjective QA with consistent, data-driven scoring.

Implementation

We deployed AI-powered call scripting that provides agents with real-time suggestions during calls — recommended responses, objection handling prompts, and compliance reminders based on the conversation context. Post-call, every recording is processed through a sentiment analysis pipeline that scores customer emotion, identifies escalation points, and flags calls that need supervisor review. The automated QA system scores every call against a standardized rubric, replacing the old sample-based approach with 100% coverage. All data feeds into a supervisor dashboard with trend analysis, agent performance comparisons, and early warning indicators for at-risk accounts.

Results

First-call resolution improved by 35% within the first quarter. Customer satisfaction scores increased by 28% as measured by post-call surveys. QA coverage went from roughly 5% of calls to 100%, and the consistency of scoring reduced agent disputes about performance reviews. Supervisor time spent on manual QA dropped by over 80%, freeing them to focus on coaching and training. The operation successfully scaled from 40 to 65 agents while maintaining quality scores.