A camera that inspects, counts, and reads what someone now does by eye

At the end of the line, someone looks at every product before it goes into the box. In the warehouse, a colleague recounts the pallets because the stock in the system is never quite right. At the gate, someone copies the license plate of every truck onto a list. This work demands attention all day, and by the end of the afternoon something still slips through. Computer vision is a camera with a model that takes over this kind of looking work.

What a camera can and cannot see

Computer vision is not magic. It is a model trained on examples of what is good and what is wrong, which then measures every new image against that standard. It works well for tasks that a person also does in half a second: is the weld closed, is the label straight, is there a pallet in spot B14, how many crates are on the truck, which size is this part. It works poorly for judgments that even your best employee is unsure about. If two experienced people disagree on what counts as a reject, the model will not learn it either.

That is why we always start by watching. We stand next to the person who inspects or counts now and ask what exactly they see. Sometimes it turns out that a load cell or a barcode scanner is enough, and then we say so. We use a camera when the image really matters. If the task is text on paper rather than products, you are better served by Document processing (OCR + AI).

From a few hundred photos to a working prototype

We do not need a mountain of images to start. A few hundred photos of good and bad cases are usually enough for a first version. If you do not have them, we mount a camera and let it run for a week. Then we train a model, often starting from an existing base model that we adjust to your products. Within 10 business days, there is a prototype running on your own images.

You run it alongside your current check and compare. Where the model is unsure, it shows that instead of guessing, and those doubtful cases go to a person. In the first weeks, you see exactly how reliable it is before you start to depend on it. Light, dust, vibration, and reflections matter a lot. That is why we test in your hall and not in our office. If the camera is not stable enough in that spot, we move it, or we tell you it will not work that way.

The result lands in your own system

A model that only lights a green or red lamp is a nice demo, not a solution. The value lies in the result landing somewhere. A rejected product is recorded with a photo and a timestamp, so at the end of the week you see which batch or which machine produced the most rejects. A counted pallet corrects the stock in your ERP or WMS right away. A recognized license plate opens the barrier and puts the arrival in your planning. We do this connection work through System integration, so your people have nothing to retype.

If the camera hangs on a machine that also measures temperature or vibration, we combine that stream with Forecasting and predictive maintenance. Then you see not only that a product is wrong, but also that the machine is about to make another one. And if looking work is only one of the places where hours leak away at your company, it often starts better with an AI process scan, so you first know which process returns the most.

Images stay with you

Camera images are sensitive. They show products that your competitor would like to see, and sometimes people who did not ask to be there. That is why the model preferably runs on a small computer next to the camera, on your own premises. The images do not have to leave the building. If more computing power is needed, we use a server inside the EU and not a service that keeps your images to learn from them.

If employees or visitors appear in the images, we make them unrecognizable before anything is stored. Together with GDPR compliance, we record what you keep and for how long. The trained model, the images, and the code are yours. You own them and can continue without us later.