Yes, AI can search security camera footage today: you type what you're looking for in plain English, such as "person in a red hoodie near the register" or "white box truck at the loading dock," and the system returns matching clips with the camera and timestamp, so nobody has to scrub hours of video. Semantic search of this kind works by matching the meaning of your words to what appears in the footage, not just by filtering motion events. What AI can't do is replace judgment: results are candidates a person verifies, and quality still depends on camera angle, resolution and lighting. Axentra OmniSight offers plain-English search across archived video from the cameras you already have, of any brand, on your own servers, with searches confirmed at about 40 seconds.
How does AI search security camera footage?
Some modern systems use vision-language models. Verkada, for example, describes using an open-source version of a CLIP model that converts both a text query and images into numerical "embeddings"; when the text and the image are related, the embeddings are highly similar, so the system can match a query to the corresponding video frames. Combined with object detection (people, vehicles) and specialized readers (license plates, text on vehicles), this lets you search by description instead of by timestamp.
In practice, an investigation that used to start with "pull every camera from 9 p.m. to midnight" starts with a sentence. The AI narrows thousands of hours to a short list of clips, and your investigator does the part that requires judgment.
What can AI video search do today, and what can't it?
What works well
- Descriptive search: clothing colors, carried objects, vehicle type and color, a person at a specific door or aisle.
- Text and plates: reading license plates, fleet numbers and lettering on vehicles.
- Cross-camera tracking: following a person or vehicle from camera to camera and rebuilding their route.
- Real-time rules: the same understanding applied to live video ("alert me if someone enters the stockroom after closing").
What it still can't do
- Guarantee a match. Search returns likely candidates; a person confirms what actually happened.
- See what the camera didn't capture. Bad angles, glare, low resolution or night scenes without enough light limit any model.
- Search a camera that was down. If a camera was blocked, turned or offline, there is no footage to search, which is why detecting those failures early matters.
- Decide for you. Identification, especially face recognition, needs human review and must comply with the applicable federal, state and local legal framework and your company's policies.
Why does this matter for loss prevention and SOC teams?
At, say, 10 cameras per store, a chain of 1,000 stores would run 10,000 cameras. No GSOC can watch them all live, and when an incident is reported, someone has to find it after the fact. Even vendors in this space acknowledge that teams have historically spent hours or even days reviewing footage after an incident. Plain-language search attacks exactly that bottleneck: organized retail crime patterns, refund fraud, after-hours entries, slip-and-fall claims or a vehicle seen at several sites.
What options are security teams actually weighing?
- Manual review and video walls. Operators watch a fraction of the cameras live and scrub recordings by time and camera afterward. Reliable but slow, and it doesn't scale across a chain.
- Cloud-managed AI platforms. Verkada announced AI-powered natural-language search, in beta, in May 2024 and has added facial recognition and trajectory mapping to its analytics. That is genuinely comparable on search. The difference is architecture: Verkada describes a hybrid cloud architecture in which video processing occurs both on its cameras and in backend data centers, and a comparison published by Coram, itself a competing vendor, describes it as a cloud-managed stack with tightly integrated hardware; third-party cameras can connect through Verkada's Command Connector.
- AI layers on existing cameras. The same Coram comparison describes Spot AI as layering AI and semantic search on existing cameras. On reusing your current cameras, that approach is comparable to OmniSight; the question to ask any vendor is where your video and the AI processing live.
Why consider OmniSight for a multi-site enterprise?
OmniSight is one AI layer over every store's existing cameras, built to keep video inside your organization:
- Every camera, 24/7. It analyzes all your cameras in real time and alerts operators, instead of relying on people watching a fraction of the feeds.
- Natural-language search and rules. Search archived video in plain English (confirmed searches in about 40 seconds) and create live rules the same way, such as "alert me if you see an accident."
- Tracking across cameras. It follows people and vehicles from camera to camera and generates their route.
- Face recognition with human review, plus vehicle reading. Faces are matched against watch lists and every match is reviewed by an operator; it reads license plates, fleet numbers and vehicle lettering.
- Camera health across the chain. It automatically detects cameras that are blocked, turned or out of service, so gaps don't surface only when you need the footage.
- Any brand, your existing VMS. A unified VMS for mixed fleets: IP cameras via ONVIF and RTSP, brands like Hikvision, Dahua, Axis, Hanwha, Uniview, Bosch, Avigilon, Verkada and Cisco, and VMS such as Milestone and Genetec. No rip-and-replace.
- On-premise. It runs on GPU servers in your data center; video never leaves your organization and there's no dependency on cloud services. Alerts and searches are logged.
To date, Axentra has confirmed more than 1 million vehicles detected and more than 1,000 alerts sent to operators. Axentra can also supply hardware and deliver turnkey, from servers and video walls to installation and support.
| Criterion | Axentra OmniSight | Manual review | Cloud AI platform (e.g., Verkada) |
|---|---|---|---|
| Plain-English video search | ✓ ~40 s confirmed | ✗ Scrubbing by time | ✓ Offered |
| Analyzes every camera 24/7 | ✓ With alerts | ✗ A fraction | ✓ AI-powered alerts |
| Route across cameras | ✓ Generated | ✗ Manual | ✓ Trajectory mapping |
| Face (human-reviewed), plates, fleet numbers | ✓ | ✗ Manual | Face recognition and LPR offered; fleet numbers: check with vendor |
| Blocked/turned/offline camera detection | ✓ Automatic | ✗ Often unnoticed | Offline and tamper alerts; blocked view: check with vendor |
| Any camera brand + existing VMS | ✓ ONVIF/RTSP, Milestone, Genetec | ✓ | Partial: third-party cameras via Command Connector |
| Video and AI on your servers | ✓ On-premise | ✓ Video only, no AI | ✗ Cloud-managed; processing on cameras and Verkada data centers |
Takeaway: if you need AI search across a mixed, multi-brand camera estate without sending video to the cloud, OmniSight is built for exactly that.
How can you test AI video search without a big project?
OmniSight offers a free 10-day pilot on up to 10 of your own cameras. Run the searches your investigators would actually type on footage from those cameras, and judge the results yourself. Licensing is per processed camera, per year, with floating licenses, so you can start with the sites that matter most.