Solutions & Use Cases

Software that detects licence plates in camera footage and reads them as text, so a plate becomes something you can search, alert on and retain -- rather than an image someone has to find by scrubbing. The same capability is called LPR in North America and ANPR elsewhere; this is both.

No. Recognition runs on streams from the cameras already installed, over RTSP and ONVIF, alongside the existing VMS or NVR. Read reliability still depends on what the camera can see -- angle, lighting, distance and resolution matter, and a camera positioned for general surveillance will not match a purpose-mounted gate camera.

Plates of interest go on a per-class text watchlist. When a read matches, an alert fires with the frame attached, and confidence thresholds are configurable per class. The watchlist governs alerting only -- every read is captured regardless, so you can search history for a plate that was never listed.

Yes. Re-identification keeps an object's identity as it moves through a scene, disappears behind an obstruction, or passes from one camera's view into another's, so a vehicle's path across an estate is assembled rather than reconstructed by hand.

Not today. Live recognition galleries currently match face subjects; vehicle and object enrollment are planned. Vehicle alerting works through the plate text watchlist, and vehicle detection and tracking work on appearance without enrollment.

No. Parking management -- permits, payments, enforcement workflows -- is a dedicated category with its own systems. This is the recognition, alerting and investigation layer; a read can be passed to other systems through APIs and workflows.

Reads and footage are held under role-based access with every access audited, retention aged by the policy you set, and the record carries chain of custody for use as evidence. Deployment can be on-premises or fully air-gapped, so plate data stays inside your boundary.

Yes. A read or a watchlist match can trigger an event-driven workflow -- log it, notify someone, escalate, or pass a validated event to an external system through your integrations. What runs is defined by you, with human approval where you want it.

Accuracy is governed by per-class confidence thresholds you control, and it inherits the physics of the camera: a clean, well-lit, well-angled view reads reliably; a distant plate at night reads worse. Low-confidence reads can be routed to a person rather than acted on.

Yes. Sensor alerts and connected records can be correlated with camera events, and the AI can reason across the sources you authorize when it investigates. What connects is configured per deployment.

No. Your dispatch, records and mapping systems stay the systems of record. Verified events flow into them through integrations, and the room keeps working the tools it knows.

A video wall shows everything and tells you nothing until someone happens to look. This watches every feed all the time, filters the noise, checks what matters and interrupts the room only with verified incidents. Coverage stops being limited by headcount.

On premises or in a government cloud under your control. For live analysis across a large network, on premises is what most cities choose. Multi-agency access runs under role based rules with every access logged.

Your city does, in writing. Every action is sorted into what runs automatically, what waits for a named person and what is never automated. The AI can raise urgency but it can never quietly cancel an alert.

AI that watches your existing cameras for safety events, turns incidents into documented evidence with chain of custody, drafts the reports, and delivers corrective training with proof of completion, one loop in one platform.

No. AI Live Insight connects to any IP camera or VMS over RTSP and ONVIF, including vehicle-mounted cameras, and rolls out in phases starting with the highest-risk areas.

Footage, snapshots, statements, and documents become one incident record in DEMS with WORM audit logs, hashing, and chain of custody, so what you show the regulator or insurer is provably what the camera saw.

Findings become courses in EnterpriseTube with quizzes, SCORM and LTI, and certification, and completion is tracked per worker, evidence that the correction actually reached the floor.

In your cloud or on-premises. Camera streams and incident evidence stay inside your boundary.

Software that turns the cameras a district or university already owns into a continuously watched safety layer. Computer vision detects the conditions you define -- after-hours entry, aggressive physical interaction, crowding, a person down, a brandished weapon -- and a policy-controlled workflow decides who is notified and what happens next, while the footage is preserved as a record.