Architected a local-first video pipeline to monitor livestock in low-connectivity rural areas, bypassing cloud latency and reducing ingress costs by 90%.
The Problem
Livestock monitoring in rural areas suffers from poor internet connectivity and prohibitive cloud ingress costs for video streams. Traditional off-the-shelf NVR solutions were too bandwidth-heavy and lacked the specialized AI capabilities needed for cattle management. Video streams would frequently disconnect, and cloud-based processing was both expensive and unreliable in areas with limited connectivity.
The Solution
Engineered a proprietary Edge AI architecture using Raspberry Pi 5 clusters as local compute nodes. Built custom NVR software that processes RTSP video streams locally, running computer vision models on-device for real-time livestock monitoring. Implemented a "store-and-forward" mechanism that queues critical data and intelligently syncs when connectivity is available, ensuring zero data loss. The system operates autonomously for days without internet access, maintaining full monitoring capabilities.
The Impact
Successfully deployed a robust, low-bandwidth monitoring system that reduced cloud costs by 90% while improving reliability to 100% uptime for critical monitoring functions. The local-first approach eliminated dependency on consistent internet connectivity, and the custom NVR solution provided specialized AI insights for cattle behavior and health that off-the-shelf tools could not match. Ranchers gained real-time monitoring capabilities even in the most remote locations.