# Every image, understood · Images · Dscvry > How the AI understands images: species and individuals, injuries and condition, defects, and the text on nameplates and labels, with every label tied to the region it came from. Source: https://dscvryai.com/technology/sense/images/ # Every image, understood. What's in it. What's wrong with it. What it means. What the AI can see in a picture · How it works · 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. ### Too many to review. A single camera trap or inspection line produces thousands of images a day. Most are never opened. ### 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. Name the animal. Then name the individual. ### Species & individuals Species first, then the individual where markings allow it: ear notches, horn shape, stripes and scars matched against your own records. See it in wildlife monitoring → https://dscvryai.com/use-cases/wildlife-monitoring/ Notice when something is wrong with it. ### Injury & condition Posture, gait, wounds and body condition across a burst of frames, so an injured animal is raised the morning it is seen, not the week after. See it in wildlife monitoring → https://dscvryai.com/use-cases/wildlife-monitoring/ Find the crack a tired eye would miss. ### Defects Cracks, burrs, marks and missing features, measured and judged against your acceptance limits and your own golden samples. See it in defect inspection → https://dscvryai.com/use-cases/defect-inspection/ Read what's written on the thing itself. ### Labels & markings Nameplates, labels, tags and gauges read straight from a site photo, so the record matches the equipment that is actually installed. See it in plant restart → https://dscvryai.com/use-cases/plant-restart/ ## 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 image Sense Camera traps, line cameras, drones and site photos. Empty-frame filter Rules & code Wind, grass and shadows dropped by a simple check. Classification Specialized model A small model names the species, the part or the scene. Close look Specialized model Gait, cracks and markings examined only where needed. Flags Check → 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](https://dscvryai.com/technology/check/) - ### Tuned to your subjects. Adapted to your species, parts and defects only with your explicit permission. [Specialized models](https://dscvryai.com/technology/models/) - ### Runs where the images are. On a base-camp machine, beside the line, or fully air-gapped. [Deployment options](https://dscvryai.com/deployment/) ## Where it shows up. Problems that start with a picture. Which animals passed, and are they hurt? ### Wildlife monitoring Identify species, spot injuries and flag poachers from camera traps. See how → https://dscvryai.com/use-cases/wildlife-monitoring/ Is this part defective? ### Defect inspection Catch defects on the line, not at the customer. See how → https://dscvryai.com/use-cases/defect-inspection/ Is the supplier’s evidence complete? ### Supplier quality Find missing or inconsistent evidence. See how → https://dscvryai.com/use-cases/supplier-quality/ What here isn’t safe? ### Safety checks Flag what’s unsafe or incomplete. See how → https://dscvryai.com/use-cases/safety-checks/ ## 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 hosted · ## The 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 → https://dscvryai.com/technology/sense/ ### Understand From piles of paper to records you can use. Explore Understand → https://dscvryai.com/technology/understand/ ### Check Every line compared. Only the gaps reach you. Explore Check → https://dscvryai.com/technology/check/ ### Investigate Follow the evidence across systems. Find out what happened, and why. Explore Investigate → https://dscvryai.com/technology/investigate/ ### Anticipate Know what comes next, and plan for it. Explore Anticipate → https://dscvryai.com/technology/anticipate/ ### Operate The next step, taken. The reason, recorded. Explore Operate → https://dscvryai.com/technology/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. 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