Insight — AI-Powered Supply Chain Tracker
Client: GlobalLogistics Corp
25%
Reduction in delivery delays
1M+
Events processed daily
99.99%
System availability at peak
5x
Faster data visualization
The Project
GlobalLogistics needed to transform their manual tracking processes into a predictive AI engine that could foresee supply chain delays before they happened.
What We Were Up Against
The system needed to process million of events from GPS sensors and shipping logs. We required an architecture that could handle heavy computational loads (Node.js/NestJS), store massive telemetry data (MongoDB), and remain 100% type-safe for enterprise scale (TypeScript).
How We Solved It
We architected a high-fidelity SaaS dashboard using TypeScript and NestJS. We implemented a MongoDB cluster for event logs, utilized Docker and Kubernetes on AWS for auto-scaling, and integrated an AI model server that predicts delivery bottlenecks based on historical data.
Technologies Used
How We Delivered
Data Architecture
Designed a schema-less MongoDB architecture to ingest high-velocity telemetry data from 5,000+ active fleet vehicles.
Enterprise Backend
Built a rigorous TypeScript-based microservices layer with NestJS, ensuring zero data loss and high-performance event processing.
AI & Scalability
Deployed the engine on Kubernetes (AWS EKS) with predictive auto-scaling to handle sudden surges in data transmission.
“The AI insights provided by SKN IT have revolutionized our fleet management. We no longer just track results; we anticipate problems and solve them before they impact our customers.”
Li Wei
VP of Logistics, GlobalLogistics
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