Case Study

Atlas Logistics

An AI-powered supply chain orchestration platform that reduced shipping costs by 28% and delivery times by 35% for a logistics network spanning 14 countries.

SaaSAI
ClientAtlas Supply Co
Services
SaaSAI
Year2025

The
Blueprint

01The Challenge

Atlas Supply Co managed a logistics network across 14 countries with 3 different legacy ERP systems, manual route planning in spreadsheets, and zero real-time visibility into shipment status. Fuel costs had increased 40% year-over-year, and customer complaints about late deliveries were at an all-time high. They needed an intelligent platform that could unify their operations, optimise routes in real time, and predict disruptions before they happened.

02The Approach

We built an event-driven microservices architecture using Node.js and Kafka that ingests real-time GPS, weather, traffic, and port congestion data. The ML route optimisation engine, trained on 3 years of historical shipping data, generates optimal route plans that adapt dynamically. We implemented a predictive disruption system using gradient-boosted decision trees that alerts operators 6-12 hours before delays occur. The React dashboard provides real-time fleet visualisation on Mapbox, automated carrier negotiations via API integrations, and role-based analytics for executives, dispatchers, and drivers.

Visual Gallery

High-fidelity designs and the final shipped product in action.

Route optimisation dashboard
Fleet tracking view

The Results

28%

Cost Savings

35%

Delivery Speed

94%

Predictions