Custom AI Route Optimization for Your 10-20 Vehicle Fleet

Many logistics companies start by planning routes with Google Maps. This works for a single vehicle but fails for a fleet. It cannot handle multi-stop sequencing, vehicle capacity constraints, or specific customer delivery windows. A dispatcher ends up with a dozen browser tabs open, manually trying to solve a complex optimization problem.

Off-the-shelf planners like Route4Me or OptimoRoute are a step up, but they are rigid. Their algorithms are generic and cannot be customized for your specific business logic. For example, consider a food distributor with 15 refrigerated trucks. If a high-value restaurant client places an emergency mid-day order, the dispatcher must manually find the nearest driver with enough refrigerated capacity. The SaaS planner cannot re-optimize the entire fleet's routes on the fly to accommodate this change without disrupting every other delivery.

Even sophisticated fleet management platforms like Samsara or Motive offer routing modules that are often rule-based, not dynamically optimized. They can show you where your trucks are, but their planning tools cannot learn from your past performance. They do not know that the loading dock at a specific grocery store always adds 25 minutes to the stop time, regardless of what the map says. The structural problem is that these are one-size-fits-all products. They offer configuration, but they do not allow true customization of the core optimization engine itself, forcing your operations to adapt to the software instead of the other way around.

The first step is a data and process audit. Syntora would connect to your existing systems, such as your TMS, ELD, and order management platform, to understand your data flow. We map every operational constraint: vehicle capacities by weight and volume, driver hours of service (HOS), customer time windows, and any unwritten rules your dispatchers currently use. This discovery phase results in a clear technical plan you approve before any code is written.

The core of the system would be a Python service using Google's OR-Tools library to solve the Vehicle Routing Problem (VRP). This engine would be wrapped in a FastAPI service deployed on AWS Lambda, allowing it to scale on demand and keep hosting costs under $50 per month. The service would ingest real-time truck locations from your ELD's API and order data from your TMS. For unstructured order requests via email, the Claude API can parse details like addresses and delivery times into structured data for the optimizer.

The delivered system is a simple web interface for your dispatcher, showing the optimal route for every vehicle on a map. More importantly, it provides an API that pushes the route manifests directly to your drivers' mobile devices or back into your TMS. When a new order arrives, the dispatcher can trigger a re-optimization for the entire fleet, with updated routes delivered in under 60 seconds. You receive the full source code and documentation, ensuring you own and control this critical business logic.