What We Build
Video AI for Safety Compliance & Operational Intelligence
Video-based AI intelligence is the automated analysis of recorded or streamed operational footage to produce structured, queryable records of what happened — which procedures were followed, which safety requirements were met, and which events fall outside the expected pattern.
Industrial sites generate an enormous amount of video and almost none of it is ever watched. Cameras record continuously, footage is retained for a retention window, and it is reviewed only after an incident — at which point it explains what went wrong rather than preventing it. The information was there the whole time; nobody had the capacity to look at it.
Video AI converts that footage from a passive archive into a structured record. Instead of hours of unwatched video, you get a queryable log: which procedures were completed in the required sequence, where required protective equipment was missing, when a person entered a restricted zone, which events departed from normal.
Deployed well, this changes safety and quality work from retrospective to current. Deployed badly, it becomes surveillance of individual workers, which is both a poor outcome and, in many jurisdictions, a legal problem. The design decisions that separate the two are made early and deliberately.
What we build
Systems are built around specific, agreed detection objectives rather than general-purpose watching, because a narrow objective can be evaluated, tuned and trusted.
- PPE and safety compliance detection — helmets, high-visibility clothing, harnesses, eye protection
- Restricted and exclusion zone monitoring, including proximity between people and moving equipment
- Standard operating procedure adherence, verifying that steps occurred in the required order
- Quality and defect inspection on production output
- Anomaly detection against an established baseline of normal operation
- Structured event logs with timestamps and clip references, queryable alongside your other operational data
- Alerting and escalation routing for events that need a response now rather than in a report
Privacy and workforce trust
The distinction that matters is between monitoring a process and monitoring a person. Systems we build detect conditions and events, not individuals: the output is that a requirement was unmet in a zone at a time, not that a named worker was at fault. Where the deployment context allows it, face blurring and identity suppression are applied at ingestion so identifying information is never stored.
Retention is scoped to what the objective requires, and access is logged. These choices need to be settled with your works council, safety committee or employee representatives before deployment rather than after — a technically excellent system that the workforce experiences as surveillance will not survive contact with the floor, and should not.
Working with the cameras you have
Most sites already have adequate coverage. Analysis runs against existing CCTV and recorded footage, and the common gaps are angle and lighting rather than camera quality — issues usually resolved by repositioning a small number of units rather than replacing the estate. Where footage cannot leave the site, processing runs at the edge or on-premise, with only the structured event log leaving the boundary.
Typical applications of Video-Based AI Intelligence.
- PPE compliance monitoring across shop floors and construction sites
- Forklift and pedestrian proximity detection in warehouses
- Lockout-tagout and permit-to-work procedure verification
- Assembly and maintenance SOP adherence checking
- Visual quality inspection on production lines
- Loading dock and yard operations monitoring
- Post-incident review with automatically indexed, searchable footage
Video-Based AI Intelligence, answered.
The questions that come up on almost every first call about this capability.
Does this require new cameras?
Usually not. Most industrial sites have sufficient coverage already and analysis runs against the existing CCTV estate. What does sometimes need attention is angle and lighting for specific detection objectives — a camera positioned for general surveillance may not see hands well enough to verify a procedure step. That is typically a handful of repositioned units rather than a replacement programme, and the coverage assessment happens during discovery.
Can it run without footage leaving our site?
Yes. Edge or on-premise processing keeps video within the site boundary, with only the structured event log — timestamps, detected conditions, clip references — leaving it. For sites with limited bandwidth or strict data policies this is usually the right architecture regardless, since streaming raw video off-site is expensive and often unnecessary.
How do you keep this from becoming employee surveillance?
By designing against it. Detection targets conditions and events rather than identities, identity suppression and face blurring are applied at ingestion where the context allows, retention is scoped to the stated objective, and access is logged. Equally important is the process: the objectives, the retention period and the access rules should be agreed with employee representatives before deployment. A system introduced without that conversation tends to fail for reasons that have nothing to do with the technology.
What accuracy should we expect?
It depends heavily on the detection objective and the conditions. Clear, well-lit PPE detection is reliable; verifying a fine-motor procedure step at distance in poor light is considerably harder. We establish real figures on your footage during discovery against a human-labelled sample, and configure alert thresholds per objective — for safety-critical detections a higher false-positive rate is usually the correct trade, since a missed event costs more than a checked one.
Is this real-time or after the fact?
Both are supported and they answer different questions. Near-real-time alerting suits conditions needing immediate response, such as a person in an exclusion zone. Batch analysis of recorded footage suits compliance reporting, trend analysis and process improvement. Most deployments run both, and the first phase is often batch — it is lower risk, and it produces the evaluation data that makes real-time alerting trustworthy.
Where we apply this.
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