A large contact center operation needed to automatically detect when agents deviated from their assigned schedules—long breaks, missed logins, or idle time—to maintain service level agreements and workforce utilization targets.

Built an AI-driven adherence monitoring engine that ingests agent activity streams in real time.

Implemented pattern detection models to identify schedule deviations, extended breaks, and workflow anomalies.

Developed supervisor dashboards with real-time adherence scores, alerts, and historical trend analysis.

Integrated with existing workforce management and telephony systems for seamless data flow.

Time-series classification models for activity pattern recognition; rule-based and ML-driven anomaly detection for schedule adherence scoring.

Real-time visibility into workforce adherence, enabling proactive intervention by supervisors.

Improved service level compliance through faster detection of out-of-adherence events.

Data-driven workforce optimization with historical adherence analytics and trend reporting.

Python, React, Kafka, Time-series analysis, ML classification models, WFM integration, Telephony APIs, Real-time dashboards.