Reduce Fuel Costs for Your Fleet with Custom AI Route Optimization

Many small fleets start by using Google Maps for multi-stop routing. This approach fails because it has a low stop limit, typically 10, and it optimizes for a single vehicle, not an entire fleet. It cannot account for vehicle capacity, delivery time windows, or driver hours of service. A dispatcher cannot use it to decide which of five drivers should handle a new pickup request; they can only plan one trip at a time.

Off-the-shelf route planners like Circuit or Route4Me are a step up but introduce their own failures. Their models assume all vehicles in a fleet are identical, making them unable to properly manage mixed fleets with different capacities or refrigeration capabilities. Their per-driver pricing models become costly as a fleet grows to 20 or 30 drivers, and their APIs are often too restrictive to allow deep integration with a custom TMS or order management system.

Consider a local food distributor with a 15-vehicle fleet and over 200 daily deliveries. The dispatcher spends 90 minutes every morning manually grouping addresses into routes using a spreadsheet and Google Maps. When a driver calls in sick, the dispatcher must spend another 30 minutes manually re-assigning that driver's 15 stops to other drivers whose routes are already full. This frantic, manual rework leads to late deliveries and costly overtime.

The structural problem is that these tools are built for the most common denominator. They are multi-tenant platforms that cannot accommodate a fleet's unique business rules, like a high-value client that only accepts deliveries between 10 AM and 11 AM. They are designed to serve thousands of customers with a generic feature set, not to solve the specific operational constraints that define your business.

The first step is an audit of your current dispatch process and data. Syntora would map out your vehicle types, capacities, driver schedules, and any specific delivery constraints like time windows or priority customers. We would analyze 3 months of historical delivery manifests and addresses to establish a performance baseline. This audit produces a clear data model and a specification document you approve before any code is written.

The technical approach would use a Python-based optimization engine with Google's OR-Tools library to solve the underlying Vehicle Routing Problem (VRP). This engine would be wrapped in a FastAPI service, making it accessible via a simple API. The system would ingest daily manifests from your existing TMS or a CSV upload. For real-time adjustments, it could be connected to the TomTom Traffic API. The entire service would be deployed on AWS Lambda for event-driven processing, keeping hosting costs under $50/month for a typical small fleet.

The final deliverable is an API that integrates directly with your current TMS or a simple web interface for your dispatcher. They upload the day's delivery list and receive optimized routes for each driver in under 60 seconds. You receive the full source code, a runbook for maintenance, and complete control over the system. No recurring license fees or per-seat charges.