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Reinforcement LearningIOTLogistics

Reclaiming 4,000 Man-Hours with Autonomous Routing

From spreadsheet chaos to reinforcement learning.

4,000 hrsSaved / Month
100%Automation Rate

The Friction

The client was manually routing 500+ trucks daily using legacy software and spreadsheets. This led to sub-optimal routes and excessive fuel consumption.

The Neural Architecture

We deployed a custom Reinforcement Learning (RL) agent trained on 5 years of historical fleet data. The agent treats the fleet as a swarm, optimizing the global efficiency.

Tech Stack Deployed

PythonTensorFlowKubernetesRedisReactAWS SageMaker

Impact Report

  • Reduced fuel consumption by 18% within 90 days.
  • Eliminated 4,000 hours of manual dispatch labor per month.
  • Improved on-time delivery rate from 82% to 97%.
  • Achieved ROI break-even in 4.5 months.
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