Connect existing RTSP cameras
Use compatible camera or recorder streams, with ONVIF discovery for supported devices. Verify the stream path, credentials, and codec before rollout.
Camera compatibility guideEnterprise AI video monitoring
On-premise AI video surveillance for your existing CCTV
Tensor Dynamics Watch adds AI monitoring to compatible RTSP cameras on customer-controlled hardware. It analyzes sampled frames, keeps event history, and alerts operators while your existing recording system stays in place. Validate privacy settings, event quality, and capacity in a pilot.
- Measured scaling
- RTSP + ONVIF
- Local inference
- Auditable event history
No rip-and-replace project
Add an AI monitoring layer to the video system you already run
Your existing recording system can continue handling recording, retention, and playback. Watch reviews sampled frames from compatible streams and organizes events for operators. Test the duration and conditions of the events you care about using representative footage.
The API source kit defaults to eight feeds; the desktop grid supports up to 64. These are not site performance guarantees. Start with representative cameras and measure stream reliability, false and missed alerts, response workflow, and compute load.
Route events to your operators
Configure Telegram, Slack, Google Chat, or webhook alerts. Establish who reviews evidence and owns the response; alert delivery is not a staffed response service.
Notification and role documentationKeep events alongside existing recording
Review status events and optional critical snapshots while your NVR or VMS handles video recording and playback. Confirm evidence retention for your deployment.
Event data documentationChoose where inference runs
Use a local or controlled private model to retain inference images in your environment. Remote models and external notification services introduce separate data destinations.
Local model deployment guideCompare on-premise, private-server, and cloud inference
Choose the model deployment by image destination, hardware responsibility, and site testing. On-premise inference does not automatically disable external integrations or other network connections.
| Decision | Local model | Controlled private server | External remote model |
|---|---|---|---|
| Inference image destination | The host running the model | Your controlled model endpoint | The configured external provider |
| Hardware planning | Validate host and model support | Provision model server and networking | Check provider limits and connectivity |
| Privacy checks | Local storage and alert settings | Internal transfer, access, and alerts | Image transfer, retention terms, and costs |
| Verify before scaling | Memory and inference latency | Server load and network latency | Rate limits, cost, and service latency |
Deployment path
Start with a measurable enterprise pilot
Record the existing workflow's alert volume and operator workload, then evaluate AI on the same scenarios. Include day and night lighting, brief events, stream interruptions, and alert-recipient confirmation. Keep agreed acceptance criteria and measured results in the pilot record.
01
Choose the site and events
02
Validate camera streams
03
Test the operator workflow
04
Expand from measured results
Enterprise use cases
One event layer for security, facilities, and operations
Warehouses and loading docks
Office and campus entrances
Manufacturing and safety zones
Public infrastructure and transport
Unstaffed areas after hours
Incident investigation and handoff
Give procurement and security teams verifiable controls
Review the documented deployment, role controls, event records, and data boundaries. Check the security statement for the status of third-party certifications. Confirm model and alert destinations, retention policy, and human escalation before rollout.
Read the security and compliance statementFAQ
Enterprise CCTV questions
Does AI Watchdog replace our enterprise VMS or NVR?
Tensor Dynamics Watch adds AI monitoring to compatible streams. Keep the existing VMS or NVR for recording, retention, and playback. Confirm whether streams should come directly from cameras or through the recorder during the pilot.
Can we run AI video surveillance on-premise?
Yes, with a local or customer-controlled private model endpoint and suitable hardware. Local engine availability varies by platform and release. Review the deployment guide and test the chosen model before promising camera capacity or response time.
Does an on-premise deployment keep every image inside our network?
Only when model and integration settings enforce that boundary. A remote model receives sampled images; external alert services may receive event text or snapshots. Review inference endpoints, notifications, storage, and network access with your security team.
How many cameras can one AI monitoring server support?
The API source kit defaults to eight feeds, while the desktop grid supports up to 64. Neither defines guaranteed inference throughput. Measure model latency, memory, stream reliability, and alert quality on the intended hardware before raising capacity.
What should an enterprise AI camera monitoring pilot measure?
Agree on target events, representative day and night footage, and a human review process. Record false alerts, missed test events, time to notification, operator workload, and host resource use. Sampled-frame monitoring may miss brief events; test those explicitly before expanding.
Validate enterprise AI monitoring on representative cameras
Confirm compatibility, event quality, data boundaries, and operator workflow before deciding how to scale.
Contact the enterprise team