Architected an event-driven spatial optimization engine leveraging graph neural networks and real-time telematics streams on Kubernetes.
A multinational logistics provider managing over 4,500 active intermodal freight assets faced cascading multi-hour delays caused by unexpected weather anomalies and regional port congestion. Their legacy centralized scheduling algorithm operated on a fixed batch window (running once every 6 hours), leaving dispatchers unable to react dynamically to real-time en route disruptions.
We engineered a distributed, event-driven spatial graph engine deployed on AWS Elastic Kubernetes Service (EKS). High-throughput telematics pings from truck GPS and vessel AIS streams are ingested via Apache Kafka into a stateful stream-processing topology. When a route anomaly or congestion event is detected, custom Graph Neural Network (GNN) heuristics recalculate optimal diversion paths within 800 milliseconds, automatically dispatching revised waypoints to onboard driver tablets via WebSockets.
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