A plant with 200 cameras generates roughly 4,800 hours of footage a day.
A security team of four, watching a wall of monitors, can meaningfully attend to a fraction of one percent of it. The overwhelming majority of CCTV footage is never reviewed by anyone — and the portion that does get reviewed is reviewed afterwards, during an incident investigation.
That is the actual state of most industrial CCTV. It is not a security system. It is a very expensive recording system that occasionally helps you understand what has already gone wrong.
The gap between recording and detecting
Cameras record. Detection requires someone or something to be watching at the moment the thing happens.
Every CCTV installation is built on an unstated assumption: that a human will be watching the relevant feed at the relevant moment. Across 200 feeds and three shifts, that assumption is false almost all of the time.
The consequences are ordinary rather than dramatic. A contractor walks into a restricted zone without a helmet, and nothing happens because nobody saw. A pallet gets loaded onto the wrong vehicle at 2am. A small electrical fire smoulders for eleven minutes in an unmanned area. In each case the camera captured it perfectly, and the footage became useful only during the post-incident review.
What changes when the watching is automated
Vision IQ applies AI inference to your existing camera feeds continuously — every feed, every shift, without fatigue.
What that enables, concretely:
PPE and safety compliance — helmet, vest and other PPE detection in zones where it is mandatory, flagged as it happens rather than in a monthly audit.
Intrusion and zone alerts — unauthorised entry into restricted areas, detected at the perimeter or at the zone boundary.
Fire and smoke detection — visual detection in areas where conventional detectors are impractical, slow, or absent, including large open sheds and outdoor yards.
Cargo and dock intelligence — loading activity, dwell time, and anomalous movement at docks and gates.
Crowd density and flow — queue and congestion monitoring for both safety and process purposes.
ANPR and vehicle intelligence — number plate recognition for gate management, dwell tracking and access control.
Why false alarms are the thing that kills these projects
Anyone who has deployed motion-based analytics knows the failure mode. The system works for two weeks, generates forty alerts a night — a plastic bag, a shadow, rain, a stray dog — and by week three the guards have muted it.
An alerting system that cries wolf is worse than no alerting system, because it consumes attention and teaches the team to ignore alerts as a category.
This is where the model architecture matters more than the feature list. Vision IQ uses vision language models that interpret the scene in operational context rather than pure motion or pixel-difference detection. A person walking through a corridor is not an intrusion. The same person climbing a boundary fence at 3am is. Distinguishing those requires understanding what is happening, not just that something moved.
From enterprise Vision IQ deployments: 85% fewer false alarms and 95% detection accuracy.
The edge inference argument
Two reasons to run inference at the edge rather than streaming everything to a cloud.
Latency. A fire detection alert is worth something at second three and much less at second forty. Edge inference delivers sub-second latency because the analysis happens on-premise, next to the cameras.
Data governance. Continuous video from inside your plant is among the most sensitive data you hold — it shows your processes, your layout, your people. Streaming it to an external cloud creates a governance problem that many organisations reasonably decline to accept. With edge inference, footage stays on your premises. Vision IQ also includes built-in anonymisation, privacy-first and GDPR-ready by design, which matters for works councils, worker representatives and any European customer auditing your operations.
Deployment can be on-premise, cloud or hybrid, depending on which of these constraints applies to you.
It has to work with the cameras you already own
The single largest barrier to video analytics adoption is the assumption that it requires new cameras. In most plants it does not.
Vision IQ works with 95%+ of existing CCTV — IP and analog cameras, ONVIF and RTSP streams, any VMS or NVR. It plugs into the cameras you already own.
That changes the economics substantially. The project stops being a capital replacement of the camera estate and becomes a software layer on infrastructure that is already installed, already cabled and already powered.
Deployment on existing cameras takes 48 hours to go live.
What happens after detection
Detection without a workflow is just a more efficient way to generate noise.
Vision IQ alerts route through a real-time alerting engine to IP hooters and speakers on the floor, and to email, WhatsApp and SMS for supervisors. No-code automation workflows let you define what happens for each detection type without a developer. Multi-site monitoring consolidates across plants. API, SAP and ERP integrations push events into the systems where work actually gets tracked.
The pipeline is straightforward: Connect to the cameras you already own → Detect and analyse with edge AI watching every feed 24/7 → Act through real-time alerts, reports and workflows.
Deployment scale to date
Across enterprise Vision IQ deployments: 3,000+ cameras integrated, 75+ enterprise clients, 2,50,000+ alerts generated, 100+ projects delivered.
Built for manufacturing, logistics, oil and gas, chemicals, construction, smart cities, energy and utilities, and automotive.
Get the full Vision IQ flyer
Two pages covering all six detection categories, the vision LLM and agent architecture, edge inference and anonymisation, camera and VMS compatibility, integration options, real detections from live deployments, and enterprise deployment results.
Common Questions
FAQs
Will this work with our analog cameras?
Yes, provided the feeds are accessible through an NVR or encoder. Vision IQ supports IP and analog cameras via ONVIF and RTSP, and works with any VMS or NVR.
Does footage leave our premises?
Not unless you choose a cloud deployment. Edge inference runs on-premise, and video stays inside your network.
How do we handle worker privacy concerns?
Built-in anonymisation is included by design. Involving worker representatives before deployment, and being explicit that the system detects conditions rather than tracking individuals, is generally more effective than any technical control on its own.
Can it detect something specific to our process?
Custom detection scenarios are part of what Vision IQ agents are designed for. The starting point is a conversation about the specific condition, the camera angles available, and whether the visual signal is genuinely distinguishable.
How fast can we see it working on our own cameras?
48 hours. The standard proof point is live detections on your own CCTV, not a demo video of someone else’s plant.
