Business Case
A European telecom operator ran its contact center QA almost entirely by hand. Reviewers could only score under 1% of interactions, which left blind spots, slow feedback to agents, and inconsistent quality standards across accounts. The mandate was to evaluate every interaction with AI, without scaling QA headcount in proportion, and to do it in a way QA managers trusted enough to act on. The core question was whether AI-generated evaluations could reliably match human judgment at volume. The platform was architected to scale to 9M+ daily interactions and 20K+ users; the impact below reflects what is live in production today.
Impact
- Scaled QA coverage from under 1% to 100% of customer interactions, eliminating sampling blind spots
- Reached 82% AI accuracy against a human benchmark through systematic calibration
- 72% automation score, taking QA from a fully manual process to mostly automated
- 43,800 interactions analyzed per month, with 637,000 question evaluations automated per month
- Deployed across 51 accounts serving 9,400 active users
- 50% improvement in process efficiency
The Approach
I built a calibration framework that lets non-technical QA managers tune AI accuracy on their own, with no engineering support. The cycle ran in four steps: analysts manually evaluate a set of interactions to set a statistical benchmark, the AI pipeline is scored against that benchmark per question, keywords and descriptions are introduced to sharpen interpretation over a few iterations, then the optimized configuration is deployed to score every interaction in production.
What Made It Work
- Question design as the lever. Not every QA question automates well. Identifying automatable versus non-automatable questions upfront saved weeks of wasted calibration on impossible targets.
- Evaluator consistency over sample size. A small set of well-distributed evaluations from experienced analysts beat hundreds of inconsistent reviews. Statistical significance arrived faster than expected.
- Score normalization. Limiting scoring to automatable, required questions removed false penalties and improved perceived accuracy.
- Built for non-technical users. I cut the dashboard down to accuracy, high/medium/low labels, and clear next actions, and surfaced accuracy evolution across cycles to build trust and adoption.