Reduce Fleet Costs with Custom AI Route Optimization

Dispatchers often start with Google Maps or Waze for single-stop routing. For multi-stop routes, they might use a tool like Routific or Circuit. These tools are great for simple delivery sequences but fail when constraints get complicated. They cannot factor in a vehicle's specific maintenance schedule, a driver's remaining hours of service, or a customer's rigid 2-4 PM receiving window.

Consider a 15-truck fleet running LTL (less-than-truckload) freight. A dispatcher has 50 stops to assign. Google Maps cannot solve this "traveling salesman problem" for more than 10 stops. A tool like Circuit can create a sequence, but it treats all trucks as identical. The route planner will not know that Truck #10 is due for an oil change in 200 miles and should get a shorter route, or that a certain delivery requires a liftgate that only Truck #5 has. The dispatcher ends up manually adjusting the computer's plan, wasting an hour every morning.

The structural problem is that off-the-shelf routing tools are built for sequencing, not holistic optimization. Their data models are fixed. You cannot add custom attributes like "vehicle maintenance status" or "customer-specific unloading time" to the optimization algorithm. They are designed to find the shortest path between points, not the most profitable and compliant routing plan for an entire fleet.

The result is excess mileage, higher fuel consumption, and uneven vehicle wear. One truck gets run into the ground while another sits idle. Dispatchers burn hours on manual planning, and unexpected maintenance issues create costly downtime that a smarter system could have predicted and avoided.

The first step is an audit of your data sources. Syntora would connect to your Transportation Management System (TMS) to pull load details and your telematics provider (like Samsara or Geotab) for real-time vehicle location and diagnostics. We map all operational constraints: driver hours, vehicle capacities, delivery windows, and maintenance schedules. This audit produces a data requirements document for your approval.

The core of the system would be a Python service using Google's OR-Tools library to solve the vehicle routing problem (VRP). This service would be deployed on AWS Lambda for cost-effective, on-demand processing. It would pull data from a Supabase database that aggregates clean data from your TMS and telematics APIs. Using FastAPI, we would expose a simple API endpoint that your dispatcher can call to get an optimized plan for the day's loads. The VRP model would consider over 25 constraints, not just distance.

The delivered system provides route plans via a simple web interface or directly into your existing TMS. Your dispatcher would see a ranked list of routes for each vehicle, with estimated fuel costs and ETAs. You receive the full Python source code, a runbook for managing the AWS and Supabase components (which typically cost under $50/month to run), and full ownership. No ongoing license fees.