Too many to review.
A single camera trap or inspection line produces thousands of images a day. Most are never opened.
Every image, understood.
What's in it.
What's wrong with it.
What it means.
Wherever the work is.
Hosted
Private cloud
On-premise
Edge
Air-gapped
Most images are
never looked at.
Camera traps, inspection cameras, site photos and phone pictures pile up faster than anyone can review them. The one that matters is in there somewhere, unlabelled.
Labelled by hand.
Species, defects and conditions are tagged one image at a time, weeks behind the work.
Judged inconsistently.
Two inspectors grade the same corrosion differently, and the record says whatever each one wrote.
What the AI
can see in a picture.
Thousands of images.
The few that matter.
Not every image needs a large model. The AI drops what's empty, classifies what's left, and only looks closely where it has a reason to.
Every imageSense
Camera traps, line cameras, drones and site photos.
Empty-frame filterRules & code
Wind, grass and shadows dropped by a simple check.
ClassificationSpecialized model
A small model names the species, the part or the scene.
Close lookSpecialized model
Gait, cracks and markings examined only where needed.
FlagsCheck → Operate
Only what needs a person reaches one.
Possible injury.
Sent to the rangers.
Image flag · camera trap T-07 · 05:42. The AI points. People go and look.
Kept. One burst of 11 frames out of 1,860 images from the night.
Identified. White rhino. The ear notch matches known individual R-12.
Looked closer. The left hind leg carries less weight in 9 of the 11 frames.
Compared. R-12 walked normally past camera T-04 two days earlier.
Illustrative example. Reserve, camera, animal and times are fictional.
Built to be
checked.
An image label is only useful if someone can see why it was given.
-
Shows where it looked.
Every label comes with the region of the image it was based on.
How Check works -
Tuned to your subjects.
Adapted to your species, parts and defects only with your explicit permission.
Specialized models -
Runs where the images are.
On a base-camp machine, beside the line, or fully air-gapped.
Deployment options
Where it runs.
The AI goes to where the work is, from hosted to air-gapped.
A managed environment.
Start with a hosted service when speed of adoption and managed operations are priorities. Agree data handling, access and service requirements as part of the engagement.
Discuss hostedIn the cloud you use.
Deploy dedicated instances in your AWS, Azure, Google or Oracle account. Align the architecture with your cloud standards, operational responsibilities and integration requirements.
Discuss public cloudWithin your enterprise network.
Run it inside your own private cloud or VPC, with network access, storage and controls set around your boundary.
Discuss private cloudOn your infrastructure.
Run software, storage and local inference on your own servers. Plan capacity, integration, maintenance and support around the environment your team operates.
Discuss on-premiseClose to the operation.
Place intelligence near industrial sites and operational equipment, where latency and connectivity shape the design. Match the workload to the local compute available.
Discuss edgeWithout constant connectivity.
Operate local workflows without a continuous internet connection. Define how data is transferred, how updates are applied, and what must remain available offline.
Discuss offlineA physically isolated environment.
Plan for an installation with no external network connection. Local models, dependencies, data transfer and maintenance all become part of the deployment design.
Discuss air-gappedThe other capabilities.
Each one is tested on your own work before you rely on it.
Sense
It sees, hears and reads the work as it happens.
Explore Sense →Understand
From piles of paper to records you can use.
Explore Understand →Check
Every line compared. Only the gaps reach you.
Explore Check →Investigate
Follow the evidence across systems. Find out what happened, and why.
Explore Investigate →Anticipate
Know what comes next, and plan for it.
Explore Anticipate →Operate
The next step, taken. The reason, recorded.
Explore Operate →Bring us the work where wrong is expensive.
Start with the operational problem. We’ll work out the data, models and agents it needs, and where it should run.
Talk to us →LondonDiscovery AI Limited · United Kingdom
HyderabadReDscvry Technology Private Limited · India
ChennaiReDscvry Technology Private Limited · India