Call Criteria

Transforming Call Center QA Through Self-Evolving AI

Call Criteria came to us with a challenge familiar to many in the call center industry: how to scale quality assurance without proportionally scaling costs. Traditional human-led QA processes, while thorough, created bottlenecks that limited growth and introduced consistency challenges across evaluators.

The Challenge

Call center QA traditionally relies on human experts listening to calls and scoring them against detailed scorecards. This approach faces fundamental constraints:

• Limited scalability: Each human evaluator can only review a finite number of calls
• Cost structure: Linear relationship between call volume and QA staffing costs
• Consistency challenges: Different evaluators interpret scoring criteria differently
• Feedback delays: Time lag between call occurrence and quality feedback
• Coverage gaps: Only a small percentage of calls can be reviewed

These constraints meant that as Call Criteria's clients grew, the QA operation faced a choice: increase costs proportionally or accept reduced coverage and longer feedback cycles.

Our Solution: RLHF-Powered Data Flywheel

We architected a cybernetic system that transforms traditional QA into an intelligent automation platform. Rather than replacing human expertise, we created a human-in-the-loop (HITL) system where AI and human evaluators work together, each amplifying the other's strengths.

Self-Evolving Agentic AI

At the core of our solution are self-evolving AI agents that continuously learn and adapt. These aren't static models that degrade over time—they're agentic AI systems that autonomously improve their performance based on ongoing human feedback.

The system implements Reinforcement Learning from Human Feedback (RLHF) at production scale—a rare achievement in enterprise AI. When human evaluators review AI-scored calls, their feedback doesn't just correct individual scores; it feeds back into the training pipeline, creating a data flywheel that makes the entire system smarter over time.

The Data Flywheel in Action

Our data flywheel creates a self-reinforcing cycle of continuous improvement:

1. AI agents analyze calls and generate quality scores
2. Human experts review a subset of AI-scored calls
3. Feedback loops capture where humans agree or disagree with AI assessments
4. Continuous learning systems retrain models based on this feedback
5. Adaptive systems automatically improve scoring accuracy across all clients

This isn't a one-time training process—it's an ongoing evolution. The system gets smarter with every call reviewed, every piece of feedback provided, and every new client scorecard deployed.

The Plexus MLOps Platform

Managing this complexity requires sophisticated infrastructure. We developed Plexus, our enterprise MLOps platform, to handle the complete machine learning lifecycle:

• Model training and versioning: Track every model iteration with full lineage
• A/B testing framework: Compare model performance across different approaches
• Automated deployment pipelines: Push improvements to production safely
• Performance monitoring: Real-time tracking of model accuracy and drift
• Human feedback integration: Seamless capture and incorporation of expert input
• Multi-client orchestration: Manage hundreds of unique scorecards simultaneously

Plexus essentially functions as a custom-tailored MLFlow that handles the entire MLOps lifecycle specifically optimized for self-evolving AI agents in an RLHF system.

Automated Scorecard Generation

One of our breakthrough innovations was automating the setup process for new client scorecards. Previously, onboarding a new client required weeks of manual configuration and training. Our system now:

• Analyzes client-provided scoring criteria
• Generates initial AI models tuned to those specific requirements
• Deploys production-ready scorecards in days instead of weeks
• Begins the continuous learning process immediately

This intelligent automation dramatically reduced time-to-value for new clients while maintaining the customization that makes Call Criteria's service valuable.

Results: Production-Scale AI That Delivers

After two years of continuous production operation, the results speak for themselves:

Performance Metrics

• Orders of magnitude cost reduction: AI reviews cost a fraction of human-only QA
• Superior speed: Calls can be scored within minutes of completion
• Enhanced consistency: AI applies scoring criteria uniformly across all calls
• Increased coverage: 100% of calls can be reviewed, not just a sample
• Continuous improvement: Model accuracy increases month over month

Business Impact

• Enabled Call Criteria to scale their QA services without proportional cost increases
• Provided clients with faster feedback loops for agent coaching
• Maintained the quality and nuance of human expertise through HITL design
• Created a sustainable competitive advantage through the data flywheel effect

Technical Innovation Meets Business Value

This project represents more than just applying AI to an existing process—it's a fundamental reimagining of how quality assurance can work. By combining:

• Production-scale RLHF (a rare achievement in enterprise AI)
• Self-evolving agentic AI (systems that get smarter autonomously)
• Data flywheel architecture (sustainable competitive moat)
• Human-in-the-loop design (preserving human expertise)
• Enterprise MLOps platform (Plexus managing the complexity)

We created a system that doesn't just automate QA—it creates an adaptive system that continuously aligns itself better to human judgment over time.

The Path Forward

The success of this implementation demonstrates that continuous learning systems can deliver transformative business value when properly architected. The combination of RLHF, data flywheels, and self-evolving agents isn't just theoretical research—it's proven technology operating at scale in production environments.

This is the kind of sophisticated AI infrastructure that separates leaders from followers in the AI-driven business landscape. And it's what we bring to every engagement.