Project details
How it works:
Based on these predictions, the platform dynamically suggests optimal routes, adjusts resource allocation, and flags risk areas in real-time. The engine continuously learns from live inputs, improving accuracy over time. As a result, UrbanLogix benefits from reduced fuel consumption, quicker deliveries, and fewer disruptions—transforming traditional logistics into a smarter, data-driven operation.
Our challange:
They struggled with unpredictable delays, inefficient routes, and manual reporting, which led to rising costs and poor delivery reliability.
We implemented a predictive analytics engine using machine learning models trained on delivery, traffic, and weather data to forecast delays and optimize routing in real-time.
The AI system enabled smarter route assignments, reduced last-minute disruptions, and improved delivery timelines. It also provided dispatchers with real-time visibility into fleet performance.
The model utilized data from GPS trackers, historical delivery logs, traffic APIs, weather data, and driver shift patterns for maximum accuracy.
We handled end-to-end development—from data pipeline setup to model training, API integration, dashboard UI/UX, and performance tuning.
They cut average delivery delays by 42%, improved fuel efficiency, and saw a 30% increase in customer satisfaction, all while scaling across multiple cities.