Vision & Sensor AI
Industrial Automation
Manufacturing
Logistics
Automotive
Challenges with Public Cloud
Delayed image and video inference due to network uplink time
High cost and added latency from data egress
Bandwidth limitations make large-scale deployments difficult
What Changes with Redsand
Local compute nodes process data directly from connected cameras or sensors
Inference happens near the source, with no need for round trips to the cloud
Low-power infrastructure can be deployed close to edge environments without complexity
Sample Use Cases
Smart factory floor where each production line runs its own local node
Roadside cabinets processing real-time input from traffic cameras
Logistics hubs using local inference for parcel sorting and anomaly detection
Benefits
Faster response times, with inference latency reduced from hundreds of milliseconds to under fifty
Improved model accuracy by preserving full frame continuity at the source
Lower operating cost with significant savings on bandwidth and cloud data transfer
