Computer Vision

Five minutes per component. That is how long a manual visual inspection takes in manufacturing. A trained CV model handles the same in under 30 seconds — at over 90% accuracy. Computer vision teaches machines to see: cameras capture images, algorithms interpret what is in them.

Manufacturing, logistics, healthcare

In manufacturing, CV systems inspect surfaces for scratches, cracks, or color defects. Thousands of images per hour, without fatigue, without breaks.

In logistics, OCR (optical character recognition) reads delivery notes, barcodes, and freight documents automatically. In healthcare, computer vision supports radiologists with X-ray and MRI images — not as a replacement, but as a second opinion that never overlooks anything.

From CNNs to Vision Transformers

For years, convolutional neural networks (CNNs) dominated. Since 2021, Vision Transformers (ViTs) have gained traction — the same architecture that powers large language models, applied to image data. The result: better accuracy with less training data.

The global market sits at $21-28 billion (2025), manufacturing holds the largest share at 37.5%. The quickest entry point: quality control or document processing, pilot project in 6-12 weeks. Do not overhaul the entire factory — one station, one problem, one proof point.