Back to Work
Machine LearningClient: TransCorp International

AI-Powered Logistics Optimization

40% reduction in delivery costs

Logistics is a game of margins. For TransCorp International, rising fuel costs and inefficient routing were eating into profitability. Our mission was to overhaul their logistics planning with a data-driven, AI-first approach.

The Challenge

Legacy routing software was static, failing to account for real-time traffic, weather conditions, or dynamic delivery windows. This resulted in longer delivery times, excessive fuel consumption, and missed SLAs.

The Solution

We engineered a custom Reinforcement Learning model that processes historical delivery data alongside real-time inputs. The system dynamically re-routes drivers in transit, optimizing for fuel efficiency and time-to-delivery.

Key Technologies

- Predictive Modeling: LSTM networks for demand forecasting.
- Route Optimization: Genetic algorithms for solving the dynamic Vehicle Routing Problem (VRP).
- Infrastructure: Scalable AWS serverless architecture for real-time processing.

Outcomes

The results were transformative: a 40% reduction in overall delivery costs and a 25% improvement in on-time delivery rates within the first quarter of deployment.

Technologies

Machine LearningPythonAWS

More Projects

AI/ML

Predictive Maintenance Platform

$2M saved annually
Data Science

E-Commerce Recommendation Engine

35% increase in sales
Web Development

Healthcare Analytics Dashboard

60% faster insights