Too many images.
A single camera trap can fill a card in days. Most frames are wind, grass and shadows.
Which animals passed, and are they hurt?
See every animal. Spot the injured one. Every species. Every camera trap. Every injury.
See it worked throughThousands of images.
A few that matter.
Camera traps and drones produce more images than rangers can look through. Most show empty ground or common species. The injured animal is in there somewhere, a week later.
Sorted by hand.
Rangers and volunteers tag species one image at a time, weeks behind.
Injuries seen late.
A limping animal or a snare wound is noticed long after it could have been treated.
What the AI reads.
Every camera-trap image and drone pass, sorted by species, counted and checked for signs of injury as it comes in.
In
- Camera-trap images
- Drone footage
- Ranger reports
- Species list
- Individual records
- Maps
At work
- Sense Reads camera-trap images and drone footage as they arrive.
- Understand Names the species in each image and drops the empty frames.
- Check Checks posture, gait and visible wounds for signs of injury.
- Anticipate Flags animals and herds seen less often than expected.
- Operate Sends rangers the image, the location and the time.
Out
One sightings log. Every flag comes with its image.
Every image sorted.
Every injury raised.
Reserve managers see what was seen, where, and which animals need a ranger, without looking through every frame.
9 routine · 1 to check · 1 possible injury
| Camera | Sighting | Status | Detail | Time |
|---|---|---|---|---|
| Northern waterhole | ||||
| T-07 | White rhino, adult | Injury | Uneven gait, left hind leg | 05:42 |
| T-07 | Impala, herd of 14 | Identified | Routine visit | 06:10 |
| T-08 | Elephant, 3 | Identified | Bull and two cows | 06:55 |
| Eastern boundary | ||||
| T-03 | Zebra, 6 | Identified | Moving south | 04:20 |
| T-09 | Spotted hyena | Check | Seen near the fence line twice in one night | 02:14 |
| T-10 | Leopard | Identified | Known individual M-04 | 01:30 |
| T-05 | 412 frames | Dropped | Wind and grass, no animals | All night |
| Southern plains | ||||
| T-11 | Giraffe, 2 | Identified | Browsing | 07:30 |
| T-12 | Blue wildebeest, 20 | Identified | Grazing | 07:45 |
| T-13 | Kudu, 4 | Identified | Routine | 08:10 |
| T-14 | Warthog, 3 | Identified | Routine | 08:02 |
Image
Adult white rhino at the northern waterhole, 11 frames over 4 minutes.
Camera trap T-07 · burst 0542Species
White rhino. Horn shape and ear notch match known individual R-12.
Species model · individual recordsGait
The left hind leg carries less weight in 9 of 11 frames.
Posture check · frames 3–11History
R-12 was seen walking normally at T-04 two days ago.
Sightings log · T-04 · Tuesday
1Injury
1To check
9Routine
For today's patrols
- InjuryFind R-12 near the northern waterhole; check the left hind leg Rhino monitoring
- CheckWalk the eastern fence line near T-09 for damage Field ranger
- IdentifiedRe-mount T-05: 412 empty frames from wind Technician
Sign-off
- Section ranger Pending
- Reserve manager Pending
The AI sorts the images. Rangers go and look.
Illustrative example. Reserve, cameras, animals and times are fictional.
Built for the bush.
Reserves mean long distances, weak signal and small teams. The AI is built for that.
Works offline.
Runs on a machine at the base camp; images sync when the signal allows.
Deployment options ↗Knows your animals.
Individual animals are recognised from your own records and markings.
Image understanding ↗Rangers stay in charge.
The AI sorts and flags. People decide where to go and what to do.
How Operate works ↗Bring us a memory card.
Send a week of camera-trap images. We’ll show you what the AI finds in them.
Talk to us →LondonDiscovery AI Limited · United Kingdom
HyderabadReDscvry Technology Private Limited · India
ChennaiReDscvry Technology Private Limited · India