Edge AI video surveillance explained: why running AI close to your cameras means faster alerts, lower bandwidth, offline resilience and video that stays on site.
Sending every camera's video to a distant data centre is costly, slow and fragile. Edge AI fixes this by running the AI on or near the cameras, right where the video is captured. Only alerts, metadata and short event clips need to travel further.
The hardware has caught up. Compact edge servers now pack serious AI power into a small box, ordinary computers increasingly include AI processors, and some cameras carry basic detection on board. Edge AI is now practical for sites of every size.
For an intruder at the fence, a weapon in a lobby or smoke on a factory floor, an alert that arrives seconds late is an alert that arrives too late. Running AI on site removes the trip to a distant server and back, so the alert reaches the operator while the event is still unfolding.
Visylix runs its complete AI engine wherever you deploy it, from a compact edge server in a single store to a rack of servers in a city control room. Each site can run its own full instance, so local alerts never wait on a distant link.
Edge deployments keep running when the network drops, which is essential for remote infrastructure, ships and sites with unreliable links. Recording, AI and alerts carry on locally, and Visylix can recover footage held on camera SD cards after an outage between camera and server.
For organisations under GDPR, HIPAA or other data residency rules, edge processing keeps sensitive video inside the building while still delivering AI. Only alerts and summary data need to leave the site, which makes audits far simpler.
The right hardware depends on how many cameras you have, which analytics you run and where the equipment will live. A small store may need just one compact edge device. A large warehouse may need rack servers with GPUs.
Visylix takes the guesswork out of it. It ships as a Docker image for standard Intel, AMD and Arm hardware, and the same platform runs on a small edge device or a full server. You tune it to the hardware through the management console by choosing resolution, frame rate and which of the 22 AI analytics run on each camera.
On every size of deployment you get the same platform: sub second live view over WebRTC, Sudarshan automation that turns detections into alarms, talkdown and SIEM events, and the Radha AI copilot running on an on premise language model.
A great deal. When AI runs at the site, only alerts, metadata and short clips need to travel, rather than continuous video from every camera. An alert with a thumbnail is a tiny fraction of the footage behind it. The exact saving depends on resolution, retention and how often events occur, but it is what makes large estates practical on modest network links, and it cuts central storage too.
Much lower than sending video to a distant server, because there is no round trip. That matters most for intruders, weapons and industrial hazards, where even a short delay can be too late. Visylix pairs local AI with sub second live view over WebRTC, so the alert and the live picture arrive together.
Yes. Each site runs a complete Visylix instance that keeps recording, analysing and alerting when the wide area link drops. Visylix can also recover footage stored on camera SD cards after an outage between camera and server. That is why edge and hybrid deployments suit remote infrastructure, ships and sites with unreliable links.
No special hardware is required. Visylix runs on standard Intel, AMD and Arm servers, from a compact edge device to rack servers with GPUs. A small store might use one edge device, while a large warehouse might use GPU servers. The same platform works on all of them, tuned through resolution, frame rate and the analytics you choose.