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Logistics / AI Mobile

CargoLink

Cross-platform mobile app with a route-optimization model that predicts delivery ETAs and reroutes shipments around delays in real time.

Delivery ETAs quoted at pickup time are usually just a static distance-and-speed calculation — they don't account for the traffic pattern that will exist three hours from now, a driver running behind on a previous stop, or a warehouse queue building up at the destination. Customers see a promise that's already wrong by the time the driver leaves the lot.

CargoLink was built mobile-first for drivers and dispatchers, with the routing intelligence living as a service both the driver app and the dispatch dashboard call into, so an ETA shown to a customer and the route a driver is actually following are always talking to the same model.

A route-optimization model continuously re-scores each active delivery against live traffic, driver position, and historical delay patterns for that corridor and time of day, and recalculates ETAs and suggested reroutes as conditions change — not just once at dispatch. When a delay is predicted to cascade into later stops, the model proposes a re-sequenced route rather than leaving dispatchers to notice the slip manually.

ETAs shown to customers track what actually happens far more closely than a static calculation, and dispatchers get an early warning on cascading delays instead of a stream of individual complaint calls after the fact — turning delivery-day firefighting into something the system flags before a customer ever notices.

Project Details

Category Logistics / AI Mobile
Completed Jul 2024

Tech Stack

Flutter Node.js TensorFlow

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