Understands events
Describe what matters in plain language. The AI reviews sampled footage and explains the event instead of returning only a motion flag.
AI video analytics for existing cameras
Tensor Dynamics Watch is AI security camera monitoring software for compatible existing RTSP cameras. It analyzes sampled frames, records events, and alerts operators. Choose local or remote models, then validate camera compatibility and event quality in a site pilot.
How smart CCTV works
Traditional CCTV records everything but still leaves people watching walls of screens. AI CCTV software adds an interpretation layer to existing video streams, helping determine what happened and whether anyone needs to respond.
The vision-language model defaults to one sampled frame every five seconds, separately from object-detection refreshes. It does not analyze every frame and may miss brief events; test the sampling interval and model with representative footage.
Connect existing cameras
Receive RTSP streams
AI interprets events
Alert the right people
AI CCTV capabilities
Describe what matters in plain language. The AI reviews sampled footage and explains the event instead of returning only a motion flag.
Send critical-event alerts through configured Telegram, Slack, Google Chat, or webhook integrations. An operator reviews the context and decides how to respond.
Review event history, evidence snapshots, and camera context without scrubbing through hours of ordinary video.
Use a local or controlled private model to keep inference images in your environment. Choosing a remote provider sends sampled images to that endpoint.
Comparison
Compare deployment requirements and verifiable workflows before choosing software for your site.
| Buying criterion | Tensor Dynamics Watch | Verify in your pilot |
|---|---|---|
| Camera compatibility | RTSP streams and supported ONVIF discovery | Exact stream URL, codec, credentials, and network access |
| Deployment and hardware | Desktop or API deployment with local or remote models | Memory, inference time, and stability at your intended camera count |
| Data privacy | Local models process images in your environment; external services are configurable | Model endpoint, event retention, and alert destinations |
| Operator workflow | Event history, optional snapshots, and chat or webhook alerts | False alerts, missed events, human review, and escalation |
With a local or controlled private model, inference images can stay in your environment, and events and snapshots can remain in your data directory. Remote models receive sampled images; external alerts may send event text or snapshots. Review each destination before enabling it.
Read about security and data boundariesFAQ
Yes, when the camera or recorder exposes a compatible RTSP stream. ONVIF discovery can help find supported cameras. Verify the exact stream URL, codec, credentials, and network access during a pilot; a camera brand alone does not guarantee compatibility.
Yes. Use a local or controlled private model. Remote model providers receive sampled images when enabled, and configured alert services can receive event text or snapshots. Review both model settings and notification destinations before deployment.
No. The vision-language model defaults to one sampled frame every five seconds, separately from object-detection refreshes. Short events may be missed. Validate the sampling interval, scene, and model with your own footage, and keep a human response process.
The desktop has a grid of up to 64 feeds; the API source kit defaults to eight. These describe the interface and default configuration, not a throughput guarantee. Actual capacity depends on hardware, model, stream settings, and measured site performance.
Apply for a free trial to verify your cameras and hardware, then request a quote based on your deployment and support needs. Hardware, installation, and third-party services are scoped separately.
Start with a free four-camera pilot and test AI monitoring in your own environment.
Apply for a pilot