Quality Pro AI

AI-powered Call Center Analytics transforming manual Quality Assessment into automated, data-driven insights, analyzes 100% of calls, improves agent performance, and boosts customer satisfaction.

Comment

Inspiration

As an ex-developer and now Manager of CX Digital and Innovation at a telecommunications company in Cameroon, I was shocked to discover our quality team still relies on manual "double-listening" to evaluate call center agents. This inefficient process means less than 1% of calls are ever analyzed!

I immediately saw an opportunity to transform this process with AI. The turning point came when I watched a YouTube video by Mike Chambers (Sr Developer Advocate for AI/ML at AWS) demonstrating Amazon Bedrock. That presentation completely shattered my concerns about the complexity of integrating AI into applications.

Quality Pro was born from this realization: combining serverless architecture with AI could solve a real business problem while giving me hands-on experience with cutting-edge AWS technologies.

What it does

Quality Pro transforms call center quality management through AWS serverless AI technology:

  1. Comprehensive Call Analysis: Automatically processes 100% of calls instead of the traditional 1%, transcribing audio and extracting actionable insights.

  2. Dual Analysis Approach:

- **Entry Call Analytics**: Evaluates live customer conversations, scoring agent performance (CSAT, NPS), analyzing conversation quality metrics (clarity, duration, courtesy), and providing immediate feedback.
- **Post-Call Survey Analysis**: Processes customer feedback, measures satisfaction, calculates churn probability, and identifies recurring issues.
  1. Interactive Dashboards: Delivers visual analytics showing key metrics such as:
- Resolution rates
- Call duration analysis 
- Courtesy scores
- Customer sentiment distribution
- Churn risk indicators
  1. AI-Driven Recommendations: Identifies specific improvement areas for agents (like reducing repetitive information, improving clarity, or enhancing issue resolution speed).

How we built it

We built Quality Pro as a fully serverless application on AWS, leveraging:

  1. Event-Driven Architecture:
    • S3 for audio storage, triggering automatic processing via SQS when new files are uploaded
    • Step Functions orchestrating the entire analysis workflow
    • DynamoDB for storing and retrieving analysis results
  2. AI Processing Pipeline:
    • Lambda functions handle specific tasks (file processing, sentiment analysis, etc.)
    • Amazon Transcribe converts speech to text with high accuracy
    • Amazon Comprehend extracting sentiment and key entities
    • Amazon Bedrock (Claude Haiku) performs sophisticated conversation analysis
  3. Web Application:
    • NextJS frontend for campaign management and data visualization
    • AWS Cognito for secure authentication
    • API Gateway and SAM for backend API development
  4. Infrastructure as Code:
    • CloudFormation templates defining all resources
    • AWS SAM for simplified serverless deployment

Test Credentials

Use the following credentials to log into the application: Test Account:

  • Email: test@qualitypro.ai
  • Password: QualityPro2025!

Challenges we ran into

Developing Quality Pro presented several significant challenges:

  1. Context-Aware Analysis: Training the AI to understand telecommunications industry terminology and common customer issues required extensive prompt engineering.

  2. Scalable Architecture Design: Building a system that could process thousands of calls concurrently while keeping costs predictable demanded careful serverless architecture planning.

  3. Cultural Adaptation: Adapting standard CX metrics to the Cameroonian context, where customer expectations and communication styles differ from Western markets.

  4. Real-time Insights Generation: Balancing comprehensive analysis with the need for timely insights required optimization of the Step Functions workflow.

Accomplishments that we're proud of

  1. 100% Serverless Architecture: Created an entirely serverless solution that scales automatically with demand and processes thousands of calls concurrently.

  2. Contextual Understanding: Our AI now correctly interprets subtle cultural nuances in Cameroonian customer interactions.

  3. Comprehensive Dashboard: Developed intuitive visualizations that present complex call analytics in an actionable format for managers and trainers.

  4. Cost Efficiency: Achieved 85% reduction in quality assessment costs compared to traditional manual methods.

What we learned

Throughout this project, we gained valuable insights:

  1. AI Prompt Engineering: Discovered techniques to extract specific metrics from conversations by carefully designing prompts for generative AI models.

  2. AWS Serverless Integration: Mastered complex integrations between multiple AWS serverless services to create a cohesive and fault-tolerant application.

  3. CX Measurement Science: Deepened our understanding of how to quantify customer experience objectively across different communication styles.

  4. Cost Optimization: Developed strategies for balancing processing depth with serverless cost management.

What's next for Quality Pro AI

We're just getting started! The roadmap includes:

  1. Launch as an Independent SaaS Business: Following this hackathon, I plan to develop Quality Pro as a commercial product through my own company, offering it as a SaaS solution to businesses across Africa.

  2. Knowledge Base Integration: Incorporating company-specific knowledge bases to provide more contextual analysis based on products, policies, and procedures.

  3. Custom Evaluation Templates: Allowing companies to define their evaluation criteria and scoring methodologies.

  4. Real-time Coaching: Developing a real-time agent coaching system that provides suggestions during live calls.

  5. Industry Expansion: Adapting the solution for banking, insurance across Africa.

  6. Multilingual Support: Adding support for more African languages and dialects.

  7. Predictive Analytics: Implementing predictive models to forecast customer satisfaction trends and proactively address issues.

  8. Integration APIs: Building connectors for popular CRM and call center management systems.

Built With

Share this project:

Updates