Build an AI Agent for Real-Time Route Adjustments

Logistics firms rely on Transportation Management Systems (TMS) like McLeod or Motive for initial route planning. These systems create efficient multi-stop routes based on static data like average travel times. They are excellent for planning a day's deliveries at 6 AM. The plan breaks at 6:01 AM when an unexpected accident closes a highway.

Consider a 30-truck local delivery fleet. At 10 AM, a major accident blocks a key bridge. The dispatcher's phone rings with a call from a driver already stuck in the backup. The dispatcher sees 5 other trucks are routed over that same bridge in the next 90 minutes. They must now manually check Google Maps for each of the 5 affected trucks, find a new route, call each driver, and verbally walk them through the turn-by-turn directions. This process takes at least 10 minutes per driver, totaling nearly an hour of reactive, high-stress work. Meanwhile, delivery windows are missed and fuel is wasted.

The structural issue is that TMS routing modules are batch-oriented, not event-driven. They are designed to solve the Vehicle Routing Problem once, at the start of the day. They lack the architecture to continuously ingest real-time event streams from GPS telematics and traffic APIs like TomTom or HERE. Their data models do not have a concept of a 'route in progress' that can be dynamically re-optimized. The TMS is a system of record, not a real-time decision engine.

The engagement would begin with an audit of your current tech stack. Syntora would map your TMS data sources, vehicle telematics provider (e.g., Samsara, Geotab), and how drivers currently receive instructions. The goal is to identify the fastest path to getting real-time location data and pushing updated routes back to drivers. This initial 2-day audit produces a technical plan you approve before any code is written.

The system would be a set of AWS Lambda functions written in Python, triggered by real-time events. A central FastAPI service would ingest GPS pings every 30 seconds and check them against live traffic data from a provider like the TomTom Traffic API. If a vehicle's speed drops below a threshold in a known congestion zone, the system triggers a rerouting calculation. Using a library like OR-Tools, the service recalculates the optimal path for the remaining stops and sends the updated route to the driver's device.

The final deliverable is not a new dashboard you have to watch. The system runs in the background and integrates with your existing workflow. Dispatchers would receive a single alert in Slack or Teams showing the original route, the problem, and the suggested new route with ETA changes. With one click, they can approve the change, which automatically pushes the new directions to the driver’s in-cab device. You receive the full source code and an operational runbook.