UPS Leverages Striim and Google BigQuery for AI-Secured Package Delivery

UPS Capital faced a growing challenge: surging online shopping volumes led to increased package theft that overwhelmed traditional security systems. The lack of real-time data processing prevented rapid risk assessment, hurting operational efficiency and consumer confidence.

Striim provided real-time data streaming and ingestion from multiple sources, feeding into Google BigQuery for advanced analytics and machine learning. This powered UPS's DeliveryDefense Address Confidence system, which assigns confidence scores to delivery locations to predict and mitigate theft risk.

Key outcomes:

Improved customer experience: greater accuracy in package handling and stronger consumer confidence
Reduced costs: fewer losses from theft and optimized delivery routes
Enhanced fraud prevention: AI/ML-driven anomaly detection built on real-time streaming data
Faster analytics: embedded vectors in streaming data improved processing efficiency and real-time analytical accuracy

The case study demonstrates how real-time data integration directly addresses logistics vulnerabilities in modern e-commerce delivery operations.

Download the full case study to learn more.

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Striim is the real-time data platform for enterprise AI. Striim unifies data from clouds, apps, and databases, protects data as it moves through the pipeline and delivers data in sub-second latency. The result is a trusted, integrated, real-time context layer to feed agents—all at enterprise scale.

Striim’s real-time context engine bridges the gap between enterprise systems and context-starved agents, powering enterprise AI with high availability, in-flight governance, and data integrity enterprises can rely on.