CIGO Tracker AI Handle Time Predictions

CIGO Tracker's AI Handle Time Prediction engine represents a significant shift in last-mile logistics from static buffering to dynamic, data-driven scheduling. Built on a proprietary machine learning framework, the system analyzes millions of historical data points—including driver performance metrics, specific location accessibility (e.g., high-rise vs. residential), cargo complexity, and seasonal trends—to predict the exact 'service time' required at each stop. By 2026, the tool has evolved to include 'Friction Scoring,' which accounts for hyper-local variables like elevator wait times and parking difficulty. The technical architecture operates as an intelligence layer on top of their core dispatching engine, utilizing recursive neural networks to refine predictions in real-time as drivers complete tasks. This reduces the 'ETA Gap'—the variance between scheduled and actual arrival times—by up to 40%, directly impacting customer satisfaction and fleet efficiency. For enterprise operators, it provides a granular view of operational bottlenecks, allowing for precise labor allocation and the elimination of costly overtime caused by under-calculated route durations.