Is anyone where they shouldn’t be?

Fence lines, gates and restricted zones watched day and night, so a person or vehicle crossing the line is raised in seconds and the fox is not.

See it worked through

Cameras everywhere.
Nobody watching.

A large site can have hundreds of cameras and a control room that sees a few of them at a time. Motion alarms fire all night, so real crossings get lost.

Alarms that cry wolf.

Foxes, rain, swaying trees and headlights set off motion alerts until operators stop trusting them.

Too many screens.

Operators watch a wall of feeds and can follow only a handful closely.

Found the next morning.

A cut fence or a vehicle in the wrong yard is often found on the recording, hours later.

What the AI reads.

Every camera on the perimeter watched against the zones, times and rules you set for each part of the site.

In

  • Perimeter camera feeds
  • Thermal and night cameras
  • Site map and zones
  • Gate and access events
  • Shift and permit schedules
  • Alarm history

At work

  1. Sense Watches every feed, in daylight, darkness and bad weather.
  2. Understand Tells people and vehicles apart from animals, weather and shadows.
  3. Check Checks each crossing against the zone, the time and who is expected there.
  4. Operate Raises an alert with the clip and the location, and logs the operator’s response.

Out

Real crossings raised in seconds, each with its clip and location.

Every crossing seen.
Every alert explained.

Control room operators see which detections were real, which were cleared and why, with the clip and the zone beside each one.

Perimeter · Distribution site · Night shiftIllustrative

6 detections · 2 alerts · 4 cleared

TimeCameraResultDetailZone
01:12C-14 · north fenceFilteredFox, from its thermal signature; no alertFence line
01:47C-03 · gate 2ExpectedNight delivery lorry in its booked 01:45 slotYard
02:20C-09 · east fenceFilteredBranch moving in the windFence line
02:58C-21 · fuel storeAlertPerson on foot inside the fence; no permit or badge eventRestricted
03:05C-22 · fuel storeAlertSame person, moving towards the tanksRestricted
04:31C-07 · car parkLoggedSecurity patrol vehicle on its routeCar park

Illustrative example. Site, cameras and events are fictional.

Built for control rooms.

The AI watches. Your operators decide what happens next.

People decide the response.

Alerts go to an operator with the clip. Nothing is dispatched automatically.

How Operate works ↗

About the site, not the person.

Detections are about zones, times and the rules on your site, not about who someone is.

How Sense works ↗

The documents and data behind this work answer other questions too.

What here isn’t safe?

Safety checks

Flag what’s unsafe or incomplete.

See how →

Which animals passed, and are they hurt?

Wildlife monitoring

Identify species, spot injuries and flag poachers from camera traps.

See how →

Bring us a night of camera footage.

We’ll show you the crossings that mattered and the alarms you could have ignored.

Talk to us →
LondonDiscovery AI Limited · United Kingdom
HyderabadReDscvry Technology Private Limited · India
ChennaiReDscvry Technology Private Limited · India