How we cut delivery times 23% for a national logistics provider using real-time ML optimization.
A national logistics provider with 850+ vehicles was struggling with inefficient routing. Their legacy system used static routes that couldn't adapt to real-world conditions—traffic, weather, driver availability, or shifting delivery windows.
We built a machine learning system that treats routing as a continuous optimization problem, not a one-time calculation.
Connected 14 data sources including GPS telemetry, traffic APIs, weather services, and historical delivery patterns into a unified streaming pipeline.
Developed ensemble models combining gradient boosting for travel time prediction with reinforcement learning for dynamic re-routing decisions.
Built a low-latency optimization engine that recalculates optimal routes every 5 minutes, pushing updates directly to driver devices.
Implemented continuous learning from actual vs. predicted outcomes, improving model accuracy by 8% month-over-month.
"Attractor Labs didn't just give us better routes—they gave us a system that gets smarter every day. Our drivers actually trust the recommendations now."
Let's explore how we can bring clarity to your operations.
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