AI industrial quality control: detect every defect, without exception

A product change does not mean starting from scratch. Psycle's software is designed to adapt quickly: when a new product reference is launched, new inspection rules are defined so it can go into production as soon as possible. You absorb changes to your product range without replacing your vision system.

False rejects decrease over time thanks to annotation. When a compliant product is wrongly rejected, your teams can flag and annotate it: the system incorporates this case and refines its decision for all vision stations. At Psycle, every correction benefits all your lines, which gradually lowers the false positive rate.

A 100% inspection can be trusted provided the system is calibrated on your real cases and its decisions remain verifiable. At Psycle, reliability comes from inspection rules defined on the basis of your expertise and from transparent indicators, never from a black box. You stay in control of what is deemed compliant, which makes automated inspection trustworthy in production.

A machine vision system detects five main families of defects, whatever the industry: cosmetic defects (scratches, stains, deformation, dents, corrosion), conformity defects (presence/absence of a component, assembly or orientation error, incorrect label or marking), dimensional defects (out-of-tolerance dimensions, misalignment, positioning), sealing, crimping or welding defects (leak-tightness, hermeticity), and the presence of foreign bodies. These inspections apply equally to food packaging, a metal part, an electronic assembly or a component in a critical environment — all areas covered by Psycle.

A vision-based quality control system combines controlled lighting, one or more cameras and decision software, installed directly on the line. Each time a product passes, the image is compared with the conformity criteria agreed with your teams, and the verdict comes in a few milliseconds, fast enough to trigger an ejection without slowing down the line.

The difficulty is almost never seeing the defect: it is distinguishing it from the product's normal variations. That is what is at stake during optical qualification, before a single line of code is written, and what separates a system that is reliable in production from a successful demonstration in the lab.