Shell Gas Station Case Study
Shell gas station operations require reliable supervision across fuel unloading, patrol inspection, personnel behavior, and incident response. Awakedata deployed an end-to-end AI analytics architecture to improve monitoring coverage, searchable data, and process-level compliance analysis.
Deployment context: The solution and outcomes below describe the systems delivered for this customer project.
Common constraints faced by distributed gas station operations.
Three coordinated layers enable scalable monitoring and multi-dimensional analysis.
Edge AI appliances connect directly to station cameras/NVR, run real-time models, compress intelligently, and trigger on-site alarms.
Regional and central hubs aggregate edge outputs for storage, playback, center-side analysis, and unified monitoring operations.
GIS visualization, event dashboards, data extraction, report export, and central live broadcast support decision-making.
From unloading safety to full-station supervision, the platform supports broad scenario automation.
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The Shell deployment addressed unloading safety across camera views: checking equipment, personnel and process steps, then connecting detected events to snapshots, playback and reports. This industrial experience informs how we help partners build their next application.
The Shell gas station solution checked operational steps and connected detections to evidence snapshots, playback, and reporting. It illustrates why industrial AI needs process context as well as object recognition.
For a similar application today, explore Studio for authoring and testing process logic, Edge for on-site execution, and Hub for event review.
SAIL — the industrial AI application language and runtime.
For help interpreting industrial requirements and authoring SAIL applications, explore Awakedata Morca.
Discuss your operation, camera setup, and requirements with our team.