Deploying an edge-to-cloud IoT architecture that ingests telemetry from 500,000 connected vehicles to predict component failures before they happen — reducing unplanned downtime by 35%.
The Challenge
Despite manufacturing vehicles with embedded sensors, the OEM had no infrastructure to act on the data being generated. Warranty claims from preventable failures were costing hundreds of millions annually, and fleet operators demanded proactive service models.
Our Approach
We built an edge-to-cloud telemetry pipeline processing 2TB of vehicle data per day. Gradient boosting models trained on historical failure patterns detect anomalies 14 days in advance on average, triggering automated service scheduling through dealer APIs.
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