Build a Route Optimization AI That Learns Your Business

Many delivery companies start with the routing features inside telematics platforms like Samsara or Motive. These are excellent for ELD compliance and GPS tracking but use simplistic routing algorithms. They primarily minimize distance and estimated time, failing to learn from historical data. They cannot account for the fact a specific loading dock always has a 30-minute delay on Tuesdays or that one driver is 20% faster on dense urban routes.

For more complex planning, teams adopt tools like Circuit or Route4Me. These are a step up for single-driver planning but cannot handle fleet-level constraints. Consider a 15-truck food distributor. The dispatcher spends two hours every morning manually assigning dozens of new orders. They try to give a driver with a liftgate-equipped truck the stops that need it, but it's a manual check. Last week, a driver ended up at a stop he couldn't service, causing a 45-minute delay and a costly redelivery.

The structural problem is that off-the-shelf tools are closed systems built for the most common denominator. You cannot inject your own business logic. They treat all drivers, vehicles, and stops as interchangeable units, which is not how a real logistics business operates. They are unable to solve multi-objective problems, like balancing route cost against the priority of a high-value customer.

The engagement would start by auditing your operational data and mapping every business constraint. Syntora would analyze your last 12-18 months of TMS and telematics data to understand service times, traffic patterns, and driver performance. We would document every rule, from vehicle capacities and driver shifts to customer-specific delivery windows and cargo requirements. This audit produces a clear plan before any code is written.

The technical approach combines a mathematical solver with a predictive machine learning model. The core system would use a VRP (Vehicle Routing Problem) solver like Google's OR-Tools to handle the combinatorial complexity. An XGBoost model, trained on your historical data, would predict the true service time at each stop. This predictive layer makes the solver's output far more accurate. The entire system would be wrapped in a FastAPI service and deployed on AWS Lambda for on-demand processing.

The final deliverable is a secure API that integrates with your existing TMS or WMS. Your dispatcher sends a list of daily orders and available vehicles; the API returns a complete, optimized plan for each driver in under 60 seconds. You receive the full Python source code, a maintenance runbook, and a Supabase dashboard to track routing performance. There are no per-seat licenses or ongoing fees outside of hosting and optional support.