Scaling computer vision from 1 to 50 cameras is challenging for most setups, but Qyvo.ai makes it seamless with edge-cloud hybrids optimised for retail and manufacturing.

Multi-camera deployments overwhelm servers with bandwidth and processing demands, driving up costs. Qyvo.ai streamlines this with intelligent inference distribution, camera calibration, and autoscaling, efficiently handling 100+ feeds based on your optimisation experience.

Common Scaling Pitfalls

Overprovisioned GPUs waste 70% capacity during off-peak hours. Latency from cloud-only processing kills real-time insights like live heatmaps. Lack of autoscaling causes surges, such as weekend retail peaks, to fail, leading to dropped frames and incomplete analytics.

Without proper synchronisation, multi-camera views misalign, reducing tracking accuracy across store aisles or classrooms. Environmental factors like varying lighting further degrade uncalibrated systems.

Qyvo.ai Deployment Blueprint

Qyvo.ai's architecture starts with edge inference directly on cameras, cutting bandwidth by 80% via on-device object detection before cloud upload. AWS autoscaling dynamically spins up GPU instances for surges, ensuring sub-second latency.

Camera calibration aligns feeds into a unified top-down map via a simple web UI, supporting RTSP streams from IP cameras. Batch processing handles 50+ feeds with NVIDIA-inspired microservices for fusion and analytics, plus dynamic offloading to balance loads across "idle" cameras.

Your spec tweaks enable deployment in days: pay-per-insight pricing scales ROI without upfront hardware overkill.

Proven Scales in Action

Retail chains run 100 cameras across hypermarkets, delivering footfall and queue metrics with 98% uptime without lag.

Our pilots scaled from 5 to 50 cameras, cutting costs 60% while boosting analytics accuracy.

Scale smart with Qyvo.ai. Contact us for a tailored deployment demo.