To choose AI video analytics software for cameras you already own, verify it ingests standard IP streams (ONVIF, RTSP, HTTP MJPEG) without proprietary firmware, runs its detections in real time on every camera at once (not one feed at a time), alerts operators in seconds, lets you search footage in plain language, and can be deployed on-prem or air-gapped if your data is sensitive. If a product needs you to replace cameras or only analyzes recordings after the fact, it fails the two tests that matter most: no rip-and-replace, and real-time.
Below is a practical checklist you can take into any 2026 evaluation or RFP, plus the questions security and public-safety leaders ask most.
Do I need to replace my cameras to add AI?
No — and this should be your first filter. Good AI video analytics is a software layer that sits on top of your existing CCTV. It reads the same video streams your VMS already receives and adds detection and search on top. If a vendor requires their own cameras or firmware, you are buying vendor lock-in, not analytics.
Ask directly: "Does this run on my current IP cameras over ONVIF or RTSP, or do I have to buy hardware from you?" The honest answer for most sites is that any standard IP camera should work.
What should AI video analytics actually be able to do?
Motion detection and simple line-crossing are not AI — they are decades-old tripwires that flood operators with false alerts. Modern analytics should understand what it is seeing and let you ask questions about it. Look for these capabilities running continuously and in parallel:
- Object, vehicle, and weapon detection — classify what's in frame, not just that pixels moved.
- Anomalous-behavior detection — flag activity that's unusual for that scene.
- Cross-camera tracking — follow a person or vehicle as it moves between cameras.
- License-plate recognition (ALPR) — read plates on the feeds you already have.
- Plain-language search — over both live and recorded footage, e.g. "find a blue truck near the OXXO between 6–7am yesterday."
The key distinction: cheaper tools run one analytic on a few cameras. Serious platforms run every capability on every camera, all the time.
The 2026 evaluation checklist
Score each vendor against these criteria:
| Criterion | What to require | |---|---| | Camera compatibility | Ingests ONVIF / RTSP / HTTP MJPEG; no proprietary firmware or forced hardware. | | Real-time, not forensic-only | Alerts operators in seconds on live feeds — not just search after an incident. | | Scale | Runs all analytics on all cameras in parallel, not a rotating subset. | | Search | Natural-language search across live and recorded video, in your operators' language. | | Deployment | On-prem, sovereign, or air-gapped options for sensitive environments. | | Integration | Layers on your existing VMS / command center — no rip-and-replace. | | Human-in-the-loop | Surfaces alerts for an operator to verify and act; it doesn't act autonomously. | | Language | Interface and search work in the languages your team actually uses. |
Real-time alerting vs. after-the-fact search — why it matters
A lot of "AI CCTV" only helps after something happens: you go back and search the recording. That's useful for investigations, but it does nothing while an incident is unfolding. The whole point of watching many cameras is that no human can stare at 200 feeds at once. The software should watch every camera continuously and push an alert to an operator within seconds of detecting something that matters — so a person can verify it and decide what to do.
Ask every vendor to demo a live alert on your camera feed, not a recorded clip. If they can only show forensic search, you're buying half a product.
On-prem vs. cloud: which do you need?
If you're a retail chain or logistics operator, cloud may be fine. If you're a C5 command center, a critical-infrastructure site, or anyone handling sensitive footage, you'll want on-prem, sovereign, or air-gapped deployment so video never leaves your control. Make sure the vendor supports the deployment model your compliance team requires — not just the one that's easiest for them to host.
How many cameras can one operator handle with AI?
That's the real ROI question. Without analytics, one operator can meaningfully watch only a handful of screens. With AI doing the watching and only surfacing verified events, the same operator can oversee hundreds of cameras and respond to what's flagged, instead of hoping they were looking at the right monitor. You're not replacing the operator — you're aiming their attention.
Where Axentra Omnisight fits
Axentra Omnisight is built exactly around the checklist above. It runs on the cameras you already have — any standard IP camera over ONVIF, RTSP, or HTTP MJPEG, with no proprietary firmware and no vendor lock-in. It runs every capability in parallel on every camera, continuously: object/vehicle/weapon detection, anomalous-behavior detection, cross-camera tracking, ALPR, and plain-language search over live and recorded footage. It watches every camera at once and alerts operators in seconds, keeping a human in the loop to verify and act. It's deployed today in metropolitan C5 command centers, national retail chains, multi-site logistics, and industrial operations, and it supports on-prem and sensitive-data deployments.
It's honestly not the right tool if you only want occasional forensic clip search on a couple of cameras — it's built for watching many cameras in real time across a site or a city.
If you want to see it alert on your own feeds, talk to our team and bring a few cameras to the demo.