A computer vision development company that works with the cameras you already own
We turn existing IP cameras into measurement instruments for safety, quality and throughput. Detection runs on a server inside your site, so footage never leaves the building, and every alert carries its evidence clip. PPE compliance, fire and smoke, spills, danger zones, inspection and dwell time, tuned on your cameras during a pilot.
Why safety and quality are still watched by people and spreadsheets
A plant with 200 cameras has 200 screens nobody watches. Supervisors do spot checks, incidents are reconstructed from memory, and defects are found by the customer. The footage that showed the missing helmet or the forklift in the walkway was recorded and then overwritten.
Buyers have been burned before. Cloud video products want footage uploaded off site, which legal and OT teams in Saudi Arabia, UAE and regulated Indian plants will not allow, and brochure accuracy figures were measured on someone else's cameras.
Our approach to AI video analytics for safety and quality
We connect to your existing camera streams, run YOLO-family detectors and tracking models on an on-premises inference server, and turn detections into evidence-backed alerts and incident tickets. The camera estate does not change; the analytics layer sits beside your video management system. This is the engineering behind our VisionGuard AI product, which packages PPE, hazard and security detection with a dashboard, ticketing and detection history.
The method matters more than the model. We survey camera placement before promising anything, because video analytics is only as good as where the lens points. Accuracy is measured on your cameras during the pilot, and each capability is enabled individually, so you pay for the detections you use.
What the solution includes
Six components, from camera survey to retention policy. The survey comes first because it decides whether the rest works.
Camera placement and coverage survey
We map each camera's field of view against the risks you want detected and tell you which zones are covered, which need a repositioned camera and which cannot be done with video.
Stream ingestion and on-premises inference
RTSP streams from existing cameras run through detectors on a GPU server or edge appliance inside your network. Footage stays on site; only alerts and metrics leave, if you allow it.
Detection, tracking and zone rules
People, vehicles and objects are detected and tracked across frames. Per-camera rules define PPE requirements, exclusion zones, dwell thresholds and counting lines, so a shop-floor helmet rule does not fire in the canteen.
Evidence-backed alerts and incident tickets
Every alert carries the clip that triggered it, routed to the right supervisor and opened as a ticket with owner, status and resolution. Detection history is searchable for audits.
Dashboards and operational analytics
Live site view, compliance trends by shift and zone, dwell-time and footfall analytics, and HSE exports, all from the detections that raise the alerts.
Privacy, consent and retention controls
Face blurring where identity is not needed, enrolled-profile recognition only with documented consent, retention per camera and role-based clip access under UAE PDPL, SDAIA PDPL and the DPDP Act 2023.
High-value use cases for video analytics solutions
The strongest cases are repetitive visual checks a person cannot sustain for eight hours.
PPE compliance on plants and construction sites
Missing helmets, vests, gloves and other configured equipment detected per zone and shift, with the clip attached so the toolbox talk is about a specific moment.
Fire, smoke, spill and smoking detection
Visible fire and smoke caught before a sensor trips, spills on walkways flagged for cleanup, and smoking in prohibited areas logged with location and time.
Crane and vehicle danger zones
People entering a crane swing radius, forklift lane or loading bay while equipment is active trigger an immediate alert to the operator and supervisor.
Visual quality inspection
Surface defects, missing components, wrong labels and packaging faults detected on the line, with images stored per unit so a defect traces back to batch and station.
Dwell time, queues and footfall
How long people wait at a counter, how many enter a store zone, where warehouse pickers spend their time, from counting lines and dwell zones drawn on your camera views.
Security and unidentified persons
Enrolled staff recognized, unidentified persons in restricted areas flagged, and altercations detected, with consent and privacy safeguards documented before enrolment starts.
How does an on-premises video analytics pipeline work?
A frame becomes a closed incident in six steps inside your network.
- 01
Pull the stream
The inference server subscribes to RTSP feeds from your cameras or VMS. No cloud upload.
- 02
Detect and track
Detectors find people and objects in each frame; a tracker follows them so one person is one event, not thirty.
- 03
Apply the zone rule
The camera's rules decide whether it matters: a person without a helmet inside the zone, a forklift crossing the pedestrian line.
- 04
Capture the evidence
The triggering frames are clipped, annotated and stored with camera, zone and timestamp.
- 05
Alert and open a ticket
The supervisor is notified on dashboard or mobile and an incident ticket opens with the clip attached.
- 06
Review and tune
The supervisor marks the alert true or false. Verdicts feed the accuracy report and threshold tuning.
Deployment, cameras and integration
Processing runs on a GPU server or edge appliance inside your site, sized to camera count and enabled detections. Multi-site estates report to one dashboard while footage stays local. The system connects to your VMS over RTSP or ONVIF, to HSE and ticketing tools through APIs, and to access-control and IoT systems where a camera event should trigger a barrier or alarm. Our edge AI practice handles hardware sizing.
Where we deliver: in Saudi Arabia, on-site processing meets SDAIA PDPL localization expectations and NCA ECC-2:2024 OT controls; in UAE, deployments follow UAE PDPL consent rules for any biometric feature; in India, plants need DPDP Act 2023 compliance and often run on constrained bandwidth, which on-premises inference solves; in United States, OSHA recordkeeping and state biometric privacy laws shape what is detected and retained. Our offices run the site surveys.
Benefits and success criteria
Accuracy is measured on your cameras during the pilot, never quoted in advance. Measures are agreed with your HSE or quality lead before any stream is connected.
Precision and recall per rule
True and false alerts per detection and camera, judged by your supervisors, reported per shift and lighting condition.
Time to acknowledge
How quickly a real alert is seen and actioned, and how many close with a documented resolution.
Camera coverage
Share of the risk zones you care about that are watched after the survey and any repositioning.
Operational effect
Compliance trend by zone, defects caught before dispatch, or queue time reduced, against the baseline.
Implementation approach
Discovery, site survey and proposal, pilot run, then go-live. A pilot usually covers one site, a few cameras and two or three detections.
- 01
Discovery with HSE and operations
Which incidents hurt most, which zones matter, who responds and what evidence an investigation needs.
- 02
Site and camera survey
Field of view, lighting, mounting height and network path checked for every candidate camera. The proposal lists what is detectable.
- 03
Install the inference server
A GPU server or edge appliance is commissioned inside your network and connected to the selected streams.
- 04
Configure rules and run the pilot
Zones, PPE requirements and thresholds are drawn per camera. Supervisors judge alerts daily.
- 05
Report accuracy and decide
Precision and recall per rule, false-alert causes and coverage gaps are presented. You decide what goes live.
- 06
Go live and extend
Alerts route to real responders, tickets go into your HSE system, and more cameras, sites or detections are added in waves.
Why Constient for computer vision
We built VisionGuard AI around on-premises processing because that is what plants, hospitals and government sites in our markets require, and we say no to detections a camera position cannot support. Our pilots end with an accuracy report your HSE lead has co-authored.
The same team covers edge hardware, data pipeline and integration into HSE and ticketing systems, drawing on our AI and data science and manufacturing work. Models are offered as modular, subscription, OEM or white-label, so integrators and camera vendors can build on them.
Frequently asked questions
How does a computer vision engagement with Constient start?
With a discovery call and a site survey. We ask which incidents and defects cost you most, then check every candidate camera for field of view, lighting and network path. You receive a proposal stating which detections are feasible on which cameras, which need repositioning, and a pilot scope with the accuracy measures your team will judge.
Does any footage leave our site?
No, unless you choose otherwise. Inference runs on a server or edge appliance inside your network. Only alerts, clips you authorize and aggregated metrics travel to a central dashboard, which can itself be hosted on your premises. This is why the solution fits SDAIA PDPL, NCA ECC-2:2024 OT controls and similar requirements.
What accuracy can we expect for PPE detection?
We do not quote a percentage before the pilot, because accuracy depends on your cameras, lighting, PPE colors and workforce. During the pilot your supervisors judge every alert and we report precision and recall per rule and camera. If a detection does not reach the threshold you set, it does not go live, and the report explains why.
How are privacy and biometric features handled?
Face recognition of enrolled profiles is optional and switched on only with documented consent and a lawful basis under UAE PDPL, SDAIA PDPL, the DPDP Act 2023 or applicable US state law. Where identity is not needed, faces are blurred in stored clips. Retention is set per camera and clip access is role-based and logged.
How long does a pilot take and how is it priced?
A single-site pilot with a handful of cameras and two or three detections typically runs 6 to 10 weeks including the survey. Survey and pilot are a fixed fee; go-live is priced per camera and enabled detection on subscription, or on OEM and white-label terms for integrators. Hardware is quoted separately at cost.
Is this the same as VisionGuard AI?
VisionGuard AI is our packaged product for safety and security detection with dashboard, ticketing and detection history ready to configure. This solution covers VisionGuard deployments plus custom vision work the product does not include, such as line-side quality inspection or warehouse dwell-time analytics. See VisionGuard AI if workplace safety is your main need.
Book a camera survey and find out what your site can detect
We will check your cameras, tell you which risks are detectable from where they stand, and scope a pilot your own HSE team will judge.