Transforming Call Center Quality Assurance with AI-Powered Automation
Challenge
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
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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
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.
- The system maintains enterprise-grade security with role-based access control and integration with Azure Active Directory.
- Comprehensive audit trails, data protection, and retention policies ensure compliance.
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.
- The system maintains enterprise-grade security with role-based access control and integration with Azure Active Directory.
- Comprehensive audit trails, data protection, and retention policies ensure compliance.
Results
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.
- The system provides the base to identify performance patterns, compliance risks, and improvement opportunities at scale
- Data-driven decisions can be made about training programs, coaching priorities, and performance management.
- Real-time alerts for compliance violations and quality issues enable immediate intervention.
Benefits
- Quick identification of issues
- Strategic, data-driven training approaches
- Reduced QA operational costs
- Improved service quality & agent performance
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.
- The system provides the base to identify performance patterns, compliance risks, and improvement opportunities at scale
- Data-driven decisions can be made about training programs, coaching priorities, and performance management.
- Real-time alerts for compliance violations and quality issues enable immediate intervention.
Benefits
- Quick identification of issues
- Strategic, data-driven training approaches
- Reduced QA operational costs
- Improved service quality & agent performance