Industrial Machine Vision and Analytics

The challenge

Camera and sensor data sat unused because there was no practical way to interpret it at scale or at the edge.

Bandwidth back to the cloud was too limited to stream raw video for central processing.

The approach

• Ran object-recognition models on edge GPUs so inference happens at the site, not in the cloud.
• Built video-stitching to assemble coherent wide-area views from multiple feeds.
• Surfaced detections and alerts into the operations layer alongside process data.
• Tuned models against real field conditions rather than ideal lab footage.

The outcome

• Situational awareness over sites operators rarely physically visit.
• Predictive signals pulled from data that had been sitting idle.
• Edge inference that works within real bandwidth constraints.

Stack