Get Custom AI Route Optimization Built for Your Fleet

Many regional trucking companies rely on the basic routing modules included with their TMS, such as McLeod LoadMaster or TMW Suite. These modules can sequence stops, but they often fail to account for complex, real-world constraints like driver-specific HOS rules, preferred customer time windows, or vehicle-specific restrictions. The logic is fixed and cannot be customized to a fleet's unique operational knowledge.

For example, consider a dispatcher for a 25-truck LTL carrier. Each morning, they export 150 stops from the TMS into a spreadsheet. They spend the next two hours in Google Maps, manually grouping stops into routes, trying to remember that one customer's dock closes at 3 PM and another route has a low bridge that a 53-foot trailer cannot pass. When a driver calls out, the dispatcher must manually re-plan five routes, a 45-minute fire drill that delays the entire fleet's departure.

SaaS tools like Route4Me or Samsara offer more advanced features but introduce a recurring per-vehicle, per-month cost that penalizes growth. Their optimizers are generic and cannot easily incorporate your company's specific business logic, such as prioritizing high-value customers or knowing which drivers are best for certain routes. You are forced to adapt your operations to the software's limitations.

The structural problem is that off-the-shelf software provides a one-size-fits-all model for a problem that is unique to every fleet. These tools cannot capture the institutional knowledge that makes your dispatchers effective. They force a choice between rigid, inadequate TMS features and expensive, inflexible SaaS products.

The engagement starts with a discovery process to map every constraint your dispatchers manage. We audit hard constraints like HOS rules, vehicle capacities, and dock times, along with soft constraints like customer preferences or driver experience levels. Syntora would analyze your TMS data exports to define a clean, repeatable input for the optimization engine.

The technical approach involves building a dedicated FastAPI service that ingests your daily manifest of stops and vehicles. This service uses Google's OR-Tools, a powerful open-source library for solving complex vehicle routing problems. The Python-based architecture allows for granular control over the optimization logic, letting us model your specific business rules directly in code. The entire system can be deployed on AWS Lambda, processing a 200-stop plan in under 30 seconds for minimal hosting costs.

The delivered system is a simple web interface for your dispatch team. They upload a single file from your TMS and receive optimized, sequenced routes for every driver. The routes can be exported as a PDF, a CSV, or pushed directly back into your TMS if it has an accessible API. You receive the full source code, a runbook for maintenance, and a system built entirely around your fleet's specific needs.