Deepvis can detect complex and unpredictable defects that are common in recycled plastic, such as black specks, short shots, overmoulding, contamination, burn marks, and geometry defects. The model is trained on annotated examples of the defect types you care about, and holds up against the material, colour and surface variation that comes with recycled feedstock.

No. Around 120 annotated images is enough to train a model. This is a large reduction compared to traditional systems that require thousands of manually labelled samples. It is however important to note that challenging applications typically perform better, the better the data.

Yes — with the right GPU hardware, Deepvis can analyze parts in real time down to 0.5-second cycle times, making it suitable for high-speed packaging and manufacturinglines.

Traditional systems rely on rule-based image processing and require reprogramming every time a product changes. Deepvis uses AI to adapt to product variation and requires no specialist for setup, making it ideal for agile manufacturers where long and complex setuptimes are out of the question.

Each unit includes a camera, AI edge computer, and software platform for inspection. Customers can purchase the full solution as a one-time investment, with optional software licensing and support packages.

If your quality control process is manual, time-consuming, and affected by frequent changes in product, lighting, or material, Deepvis is built for you. Our system is designed to reduce man-hours in visual inspection — especially in high-volume, high-variation environments where traditional systems fall short. We typically start with a discovery call and a pilot to demonstrate time savings and operational impact.