Why Edge AI is the right architecture for Indian city surveillance

Cloud-only video analytics assumes reliable, cheap bandwidth from every camera to a data centre. At Indian city scale that assumption fails on cost, on network reliability and on privacy expectations. Processing at the edge — on devices like NVIDIA Jetson placed near camera clusters — inverts the model: only detections and evidence snapshots travel upstream, cutting bandwidth by an order of magnitude and keeping alert latency under a second.

It also localizes failure: a fibre cut takes one site offline, not the whole program. The trade-off is fleet management: hundreds of edge devices need provisioning, health monitoring and over-the-air updates, which is where most pilots stall. We built Auravex around that operational reality first.