Solutions / AI Visual Operations Intelligence for Manufacturing
AI Visual Operations Intelligence for Manufacturing
The Line Stopped Four Minutes Ago. Nobody Knows Yet.
Cameras already see the floor. Reading them continuously turns that into a record of what ran, what stopped, and where the work is actually piling up -- without instrumenting every machine.
The operations loop
Trusted where the stakes are high
Pick your plant, see what the cameras already show
Lines, cells and stations. Cameras already cover the floor; what is missing is anything watching them continuously.
- Runs on the estate you have. RTSP and ONVIF over the existing cameras, no rip and replace.
- Zones you draw. Robotic cells, electrical rooms and restricted areas as virtual boundaries.
- The clip, not the shift. Event-driven recording keeps the incident, not eight hours of nothing.
Continuous plant, few people. Long stretches where nothing happens and a short window where it matters.
- Fire and smoke. Detected on camera rather than waiting on a fixed-point sensor.
- PPE for the hazard. Respirators, harnesses and gloves as well as helmets and vests.
- Inside the boundary. Self-hosted inference where footage cannot leave the plant.
Hygiene, handling and line speed. Compliance evidence that today is a paper record and a supervisor's memory.
- Evidence per event. The incident lands as a record, not a screenshot in an email.
- Zone discipline. Entry to areas that require a change of gear or a wash step.
- Retention that fits audit. Lifecycle policy on the footage, not manual housekeeping.
Forklifts and people sharing floor. The proximity events nobody logs until one becomes an injury.
- Forklift and pedestrian. Detection of both, and of the crossings where they meet.
- Near misses surface. Recurring high-risk areas visible across weeks, not one shift.
- Cross-camera. The same vehicle or person followed between cameras.
Where the record is the product. What was detected, what was changed, and who saw it, all evidentially held.
- Chain of custody. Incident media held with an audit trail from the moment it is captured.
- De-identified for sharing. Personal data removed on the copy that leaves; original untouched.
- Air-gapped where required. Full deployment with no external calls.
What You Get
What you can read off the cameras already there
Line state without machine integration
Running, stopped, blocked or unattended read visually, for the machines where direct integration does not exist or was never finished.
Stoppages, timed
An interruption raises an event when it happens rather than surfacing in a shift report the next morning.
Where the work piles up
Queue build-up, station occupancy and material flow observed across zones, so a constraint is located rather than argued about.
Situations nobody predefined
A multi-modal model describes what a scene showed, which reaches operational conditions that were never modelled as a fixed class.
Ask across the footage
Natural-language questions over the recorded estate, with the clip returned alongside the answer.
Runs where the plant runs
On-premises or air-gapped with self-hosted inference, for sites where footage does not leave.
How It Works
From camera to operational answer
Observe
Detection runs continuously over the existing cameras, tracking objects, people and zone activity across the floor.
Interpret
Events trigger a workflow that reads the frames around them and describes what was happening, including conditions that were never defined as a class.
Ask
The archive becomes answerable. Question it in plain language and get the answer with the footage it came from.
Products Inside
The products inside
Nexus
Enterprise content platform
The record underneath: ingest, storage, search, identity, retention and chain of custody.
Explore Nexus →AI Live Insight
Real-time video analytics
Detection on the cameras already installed, over RTSP and ONVIF, with event-driven recording.
Explore AI Live Insight →AI Intelligence Hub
Agentic AI platform
The workflow that runs when an event fires: scene interpretation, cited answers, human checkpoints.
Explore AI Intelligence Hub →FAQ
AI Visual Operations Intelligence for Manufacturing, asked and answered
What is AI visual operations intelligence?
It is using the cameras a plant already has as an operational sensor: reading line state, stoppages, queueing and material flow from video, so operational visibility does not depend on instrumenting every machine.
Does this replace machine integration?
No, and it is most useful where integration is absent or partial. Reading a machine's state visually -- running, stopped, blocked, unattended -- covers equipment that was never wired for telemetry, and stands alongside direct integration rather than replacing it.
Can it tell us why the line stopped?
It can tell you what the cameras saw, when, and describe the scene around the event. Establishing cause usually needs the machine's own data as well, and joining video to plant telemetry is not something to assume -- confirm what your systems can expose before designing around it.
What about situations we did not predefine?
That is where the multi-modal analysis earns its place. Rather than requiring every condition to be modelled as a class in advance, it describes what a scene showed in language, which reaches unusual or hard-to-anticipate situations.
Is the analysis real-time?
The detection is. The scene description runs on cached frames on a cadence, so it tells you what has been happening rather than reading the current frame.
Can we ask questions of past footage?
Yes. The recorded estate is searchable in natural language, and answers come back with the clip they were drawn from.
Can it run without sending video to the cloud?
Yes. Self-hosted inference supports on-premises and fully air-gapped deployment.
Point it at one line for a week
Pick a single area and a single question. We will run detection over your own cameras and show you what a week of it surfaces.