AI-Powered Visual Intelligence Platform
X.View integrates with the CCTV and video management systems you already own. It converts video into structured operational intelligence by interpreting objects, behaviour and context, validating events and triggering action.
Exceptions, activity concentration, and system health across every AI solution.
X.View dashboard, shown with sample data.
Before the cameras
X.View integrates with the camera estate you already own, so the first question is typically what the estate should be configured to detect, rather than which vision platform to purchase.
What it does
The cameras are already installed and recording. What is missing is a layer that interprets what they capture.
Through the NVR or VMS already in place. No camera replacement, no parallel recording path and no hardware overhaul.
Objects, attributes, behaviour and the zone it happened in - a person without a helmet in a restricted area, rather than a person.
Confidence, duration, zone and schedule applied before anything is raised, so a forklift parked legally at noon is not an alert.
Routed by severity to the operator, the supervisor or the system that handles it, with the evidence attached and an escalation path if nobody responds.
Every validated event retained with its evidence, so patterns by site, zone and condition become reportable rather than anecdotal.
Detections
Detection is only useful when it is specific. These are families of use cases rather than a model list. Each is a rule defined against a zone, a schedule and an action, rather than a feature that is simply switched on.
Defined per zone, not per camera
The same camera can carry different rules for different areas in its field of view, on different schedules.
Conditions that put people at risk, caught while there is still time to intervene.
Access, intrusion and movement that does not match what should be happening.
Documented standards that are verified only through informal spot checks.
Conditions that reduce throughput rather than affect safety.
How it works
X.View sits alongside the existing estate rather than replacing it. Streams are analysed, events are validated against context and rules to suppress false positives, and confirmed events become alerts, evidence and workflow.
Scroll the timeline sideways to see all six steps.
X.View processing flow
The challenge
Most organisations generate more video than they can monitor.
A camera estate produces continuous footage and almost no operational signal. Detection depends on an operator watching the right screen at the right moment, and review takes place after the incident. The infrastructure is already in place; what is missing is the layer that interprets what it captures.
Architecture
The most common objection to visual intelligence is not accuracy but the transmission of continuous video off site. X.View is designed differently: inference is performed on your network, and only an event, not a stream, leaves the site. Your cameras and your existing NVR or VMS remain in place.
The footage does not leave the site
Only the event, its confidence and a short piece of evidence travel upstream.
X.View connects to the camera estate you already have through the NVR or VMS in place. No camera replacement, no parallel recording infrastructure and no hardware overhaul.
A device on the local network takes the stream, runs inference against the deployed models, evaluates the rules for that zone and schedule, and validates the result before anything is raised.
What goes upstream is event metadata, the confidence score, a snapshot or short clip as evidence, and device health. The continuous video stays on site.
The central platform carries dashboards, alerting, workflow and integrations - and pushes approved models, rule packages, thresholds and schedules back down to each site.
New detections
Every estate has a condition for which no model yet exists. Building one is a defined scope of work with a validation gate before release, rather than a configuration option or a research project.
What must be detected, in which zone, during which hours, and what should happen when it is - alert, evidence, ticket, or all three. The definition is the specification.
Representative footage from the real environment, or one close enough to it, with only the relevant objects, zones and behaviours marked.
An existing model where one fits, tuned for your site. A trained one where it does not. Rules, thresholds and duplicate handling configured alongside it.
Tested against real conditions - lighting, angle, occlusion, crowding - and measured for missed events and false alerts before anyone is asked to trust it.
Released to the edge with its thresholds and actions. Reviewers tag live detections as valid, false alert or missed, and that feedback drives tuning which is re-validated before it ships back out.
Capabilities
01
Detection and classification of people, vehicles, equipment and relevant objects in the scene.
and 9 more below
02
Recognition of activity and movement patterns, not only the presence of an object.
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03
Interpretation against zone, time, site rules and expected operating conditions.
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04
Identification of conditions that meet a defined risk or violation threshold.
and 9 more below
Confirmation logic that suppresses false positives before anyone is alerted.
Routed notification to the right operator, team or system as the event is confirmed.
The relevant clip and frame retained and attached to the event record.
Severity-based escalation where an event is not acknowledged or resolved.
Confirmed events raised as cases or tasks with ownership and closure.
Live and historic view of events by site, zone, type and severity.
Trend and compliance reporting across sites and periods.
Connection to VMS, access control, operational systems and case management.
Centralised operation across a distributed estate with per-site configuration.
Inside X.View
This is the X.View command view: every camera and sensor monitored by AI, every exception raised with its site and camera, and each solution measured against its intended purpose. Figures shown here are illustrative.
The overview shows what needs attention now: open and critical alerts, how much of the estate is online, and how much of it AI is actually watching. Critical and high-severity exceptions are listed with the site and camera they came from.
A seven-day heatmap shows when exceptions concentrate, hour by hour, so patrols and shifts follow the real pattern. Site health shows which locations have devices offline before a blind spot becomes an incident.
X.View checks helmets and vests, restricted zones, unsafe behaviour, falls, and fire and smoke on the cameras already installed. Compliance is tracked daily, and every violation is logged with its zone and camera.
X.View counts occupied and free spaces by zone, flags illegal and double parking, blocked fire lanes and misused accessible bays, and logs potholes and surface defects for maintenance.
X.View measures how long people wait, how many are in each area against capacity, and how busy each counter is. Supervisors get an alert when a queue or an area crosses the threshold you set, not after a complaint.
In customer-facing areas, X.View reads visitor sentiment, counts footfall and measures dwell time by zone. Negative-sentiment alerts reach the floor team while the visitor is still there.
For visits that happen outside a branch, such as home blood-sample collection, X.View guides the field worker step by step in Arabic. Each step is checked by its own model: the ID card is read, the face is matched, the sample tube is checked, and the barcode keeps the chain of custody.
X.View connects to the cameras and video systems already installed. Each camera shows its live view, stream health and the AI policy it runs, such as safety compliance or perimeter protection, and every detection is drawn on the frame.
Each detection becomes an event with a time, a camera, a type and a severity. Operators work through it as a queue, from open to on hold, resolved or false positive, so nothing is lost in a video wall.
| Time | Camera | Type | Severity |
|---|---|---|---|
| 11:54 | Main entrance | Vehicle in restricted zone | Critical |
| 11:43 | East parking | Parking obstruction | High |
| 10:48 | Loading bay | Perimeter crossing | Medium |
| 09:53 | Warehouse A | PPE non-compliance | Low |
Detection rules define what each camera looks for, how confident the model must be, and where the alert goes. Alerts reach the right team on Telegram, SMS, email or a webhook into your own systems.
| Rule | Logic | Cameras | Alerts |
|---|---|---|---|
| Intrusion · perimeter | Object detection | 3 | |
| PPE · helmet | Classification | 1 | |
| Vehicle counting | Tracking | 2 | |
| Fire & smoke | Object detection | 5 |
Cameras sit alongside door and temperature sensors in the same platform. Each device shows where it is, its last heartbeat and whether recording and AI are switched on, so an offline device is caught before it matters.
Use cases
The platform is the same in each of these. What changes is what the estate is asked to watch for, and what a confirmed event should trigger.
Contractors work around energised equipment at unmanned substations, and adherence to protective equipment and exclusion zones is confirmed by an occasional supervisor visit.
Protective equipment and zone breaches are detected on the existing cameras and raised as events with the clip attached.
Commonly applied in Utilities and across other sectors
A store service desk builds a queue at predictable times and the duty manager only learns about it when a customer complains or walks out.
Queue length and service-point use are measured from the cameras already installed, and the manager is alerted as the queue forms.
Commonly applied in Retail and across other sectors
A municipality monitors parks, depots and municipal buildings from one control room, and detection depends on an operator being on the right screen at the right moment.
Unauthorised presence and after-hours movement are flagged across sites, validated against zone and time rules before anyone is notified.
Commonly applied in Government and across other sectors
Crowding and incident conditions are detected as they occur and routed to the nearest team rather than reviewed afterwards.
HealthcareEach inspection step is evidenced from the workshop cameras, so a disputed service claim is settled from the record.
AutomotiveLoitering and unusual activity in the lobby raise a validated alert while it is happening, with the clip held as evidence.
BankingPerimeter events at remote sites are confirmed centrally and escalated by severity when an alert goes unacknowledged.
TelecommunicationsAccess rules are applied per zone and per time of day, and confirmed events reach the security team as they happen.
EducationConditions that meet a defined threshold raise a case with the site, the zone and the supporting frame attached.
GovernmentIllustrative applications rather than delivered deployments. What is detectable on any estate depends on camera placement, coverage and image quality.
Common questions
No. X.View reads streams the existing VMS already manages and runs alongside it. On mixed estates - and multi-site estates usually are mixed - compatibility is confirmed system by system during architecture rather than assumed in a proposal.
On-premise, in your cloud tenancy, or in a private environment. Site bandwidth and video retention policy normally decide it, and that is an architecture decision made before commercial terms.
Candidate detections are validated against zone, time and site rules before anyone is notified, and alert credibility is tuned during the first weeks of live operation. That tuning is included in scope; a deployment that omits it generates alerts that are ignored within a month.
Your operations team, X-Shift under a managed service, or a split. The split is agreed before go-live because it changes how alerting and escalation are configured.
Next
A proof against recordings from your estate, on the detection types you actually need, with false-positive rates measured rather than asserted.
When you have the camera estate and need to know whether the detection is reliable enough to act on.
Request a proof of valueSmart Inspection & Computer Vision starts from the operational objective - safety, security, compliance or throughput - and works back to what needs detecting.
When you know the footage is underused but not yet what you want from it.
Smart Inspection & Computer VisionThe cameras are usually the part you already have. The decision worth taking time over is what a confirmed event should trigger.
Where this sits
Also relevantSolution Architecture & Systems IntegrationMonitoring & Observability
Industry applications
The platform is the same in each of these. What changes is what it is configured to do and which systems it connects to.
Beneficiary channels, case handling and compliance evidence, built as one programme.
How the portfolio applies here UtilitiesBilling, outage and connection demand handled across channels, field and back office.
How the portfolio applies here Retail & E-CommerceWhat shoppers say online, what staff do in store, measured against the same standard.
How the portfolio applies here AutomotiveAftersales service, dealer networks and the retention that depends on both.
How the portfolio applies here Energy & Oil & GasSafety compliance, contractor governance and asset condition across distributed sites.
How the portfolio applies here Manufacturing & IndustrialLine safety, quality evidence and customer service across plants and distributors.
How the portfolio applies here Aviation, Transport & MobilityPassenger service at scale, terminal conditions and disruption handling.
How the portfolio applies hereThese are the sectors where this platform is most often applied. It is not limited to them - the requirement decides, not the industry.
Built on this
A solution is what we build from this platform, scoped to your organisation, with a go-live.
Services that decide whether to deploy it Mystery Shopping & Experience Assessment Solution Architecture & Systems Integration
We run demonstrations on a journey you actually handle rather than on a script written to show the product well.
Share your sites and priority exceptions, and we will demonstrate X.View detecting them on cameras comparable to your own.
Book an X.View demo
