Transforming Call Center Quality Assurance with AI-Powered Automation
Challenges:
- Quality Assurance evaluated only 3% of 9400 daily calls across 350 agents.
- Lack of visibility into widespread performance trends and compliance risks. Inability to identify customer satisfaction drivers.
- The manual QA process consumed 2,550 hours monthly.
- QA operational cost estimated at $0.7 million annually.
Industry
Media/Nonprofit
Solutions:
AI-Powered Call Center Agent Scoring Application
Results:
Evaluation coverage from 3% to 100% of all calls. Complete visibility of agent performance across all interaction types, times, and customer segments. Quick identification of performance gaps and training needs. Reduced manual efforts and operational costs.
Location:
US
About the Client
The client is a diversified media organization with an annual revenue of approximately $700 million and a workforce of over 1,500 employees. Operating across content production, broadcasting, digital media distribution, and publishing, the company serves millions of customers worldwide. With a strong focus on innovation, it continuously adapts to emerging technologies to stay competitive in the dynamic media landscape.
The organization's call center handles an average of 9400 calls daily, through 350 customer service agents. However, their quality assurance program faced significant limitations. With only 12 dedicated QA agents, they could evaluate approximately 280 calls per day, which was just 3% of total call volume. This sampling bias prevented an accurate assessment of agent performance. Critical compliance issues or customer experience problems went undetected in the remaining 97% of calls. Coaching interventions were delayed by weeks due to insufficient data coverage.
Case Overview
The manual QA process consumed approximately 2,550 hours monthly, costing an estimated $0.7 million annually in QA labor alone. More critically, the organization lacked visibility into widespread performance trends, compliance risks, and customer satisfaction drivers. This limited their ability to proactively improve service quality and agent performance.
The organization's leadership evaluated several alternatives, including hiring additional QA staff, implementing traditional call monitoring software, and using off-the-shelf solutions. However, these approaches would scale costs linearly due to the fundamental limitation of sampling-based evaluation. Moreover, off-the-shelf software could not provide the quality and specificity of evaluation. The decision to pursue AI-powered automation was driven by the technology's unique capability to evaluate 100% of interactions while maintaining consistent scoring standards.
CHALLENGES
Roadblocks Faced In The Existing Systems
Inability to accurately assess agent performance
Limited QA coverage –only 3% calls were reviewed
Major compliance and customer experience issues went undetected
High manual QA efforts and costs –2,550 hours monthly, costing an estimated $0.7 million annually
Limitations to proactively improve service quality and agent performance
SOLUTION
Fingent’s Approach - AI-Powered Call Center Agent Scoring Application
Fingent implemented a comprehensive AI-based Call Center Agent Scoring Application built on Microsoft Azure's cloud-native serverless architecture. The solution utilizes Azure Functions and Azure OpenAI Service with multiple GPT models for intelligent cost-optimized processing. The system processes call transcripts through a sophisticated pipeline featuring pre-processing modules for text normalization and PII removal.
Advanced model-specific prompt engineering utilizing few-shot in-context learning with domain-specific knowledge injection is used to transform the organization's 10-criterion scorecard into structured evaluation instructions. Call ingestion occurs via REST API integration with the existing contact center platform. This triggers workflows that orchestrate evaluation through configurable sampling strategies—from targeted call types to full 100% coverage— for cost optimization.
The platform integrates bidirectionally with existing systems and business intelligence tools.
Comprehensive audit trails, data protection, and retention policies ensure compliance.
The system maintains enterprise-grade security with role-based access control and integration with Azure Active Directory.
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IMPLEMENTATION JOURNEY
Ensuring a Successful and Smooth AI Transition
The implementation began with a comprehensive feasibility study involving detailed analysis of the organization's existing QA criteria, call types, and evaluation processes. The team conducted extensive testing of different evaluation strategies and LLM models to identify optimal performance and cost. The development phase focused on building the core Azure-based application backend, the PostgreSQL, and the CosmosDB. This was done by establishing secure API connections with their existing contact center platform for automated transcript ingestion.
The rollout phase began with a pilot program covering specific call types and agent groups, gradually expanding to full deployment across all 350 agents. This phase involved fine-tuning based on real-world performance data, optimizing cost efficiency and processing strategies, and training managers and QA staff on the new system capabilities. Throughout the implementation, Fingent maintained a strong stakeholder engagement with regular progress updates, addressed technical challenges through iterative problem-solving, and ensured seamless integration with existing workflows to minimize operational disruption.
Developed a React-based user interface for QA supervisors and managers to review and modify AI evaluations.
Extensive testing across different call types, agent performance levels, and edge cases ensured reliable operation before deployment.
Implemented comprehensive audit trails and integration with Power BI for reporting.
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BENEFITS
Making An Impact On Client Success
The AI-powered QA solution delivered improvements across multiple dimensions of the organization's call center operations. The most significant achievement was expanding evaluation coverage from 3% to 100% of all calls—a massive increase in visibility. This comprehensive coverage eliminated sampling bias and provided the first complete picture of agent performance across all interaction types, times, and customer segments. The automation generated substantial cost savings by reducing manual QA labor requirements significantly. QA staff can now focus on strategic coaching, trend analysis, and complex cases requiring human judgment.
Coaches and managers gain access to comprehensive, objective performance data enabling fair and accurate agent assessments.
Data-driven decisions can be made about training programs, coaching priorities, and performance management.
The system provides the base to identify performance patterns, compliance risks, and improvement opportunities at scale
Real-time alerts for compliance violations and quality issues enable immediate intervention.
