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AI Parcel Monitoring and Tracking
Scope: Operations optimization using image Recognition
Industry: Logistics / Transportation
Customer: Leading US logistics company (NDA)
Timeline: 6 month POC, 12 month for software development, integration and support
TEAM
  • 2 Computer Vision Engineers
  • 2 C++ Developers
  • 1 UI Designer
  • 1 Project Manager
KEY TECHNOLOGIES
  • Python
  • C++ | TensorFlow
  • Keras
  • OpenCV OCR with Tesseract | Darknet
  • YOLO
Problem
The process of parcel identification and sorting was costly, inefficient and relied on manual labour
GOAL
Improve efficiency, reduce human error, and scale the system to meet growing demand
SOLUTION
  • Precise detection and recognition of postal labels
  • Real-time image preprocessing and label data extraction
  • Automated sorting and parcel assignment for delivery vehicles
  • Integration with existing parcel tracking systems
Ipact
Efficiency gain of 25-40%
Faster and more efficient parcel sorting and processing
Accuracy improved by 35-50%
Fewer sorting errors, even with damaged barcodes
Redundant scalability
System handled 30% volume increase without bottlenecks
30% Process Cost Reduction
Automating document review processes by extracting key information from compliance documents