In the luxury and premium segments, a micro-defect can be enough to damage a brand’s image. Barely visible scratches, embedded dust, misleading reflections on varnished or metallized surfaces… Cosmetic and packaging quality control has become a high-precision discipline.

On production lines, however, reality is complex: high throughput, material variability, frequent format changes, fine decorations, glossy or translucent parts. Human visual inspection remains essential, but it quickly reaches its limits when faced with repetition and the subtlety of certain defects.

Since 2016, Scortex has supported manufacturers through this transformation. Over the past ten years, the company has specialized in automating visual appearance inspection using artificial intelligence, with a strong focus on industrial robustness and measurable performance.

Why Is Cosmetic and Packaging Quality Control So Demanding?

Components in these sectors often combine several challenges:

• Reflective surfaces generating parasitic glare

• Extremely fine screen-printed or metallized decorations

• Sensitivity to dust and handling marks

• Strict aesthetic tolerances

• Non-perfectly repeatable product positioning on the line

Traditional systems based on fixed rules or predefined thresholds struggle to distinguish acceptable variation from critical defects. Each new product reference requires adjustments, which can be time-consuming and unstable.

The challenge is not only to detect more, but to detect accurately. Excessive rejection rates impact productivity. Insufficient rejection exposes manufacturers to costly customer complaints.

A Conformity-Based Learning Approach

To address these constraints, Scortex developed Spark, a visual inspection solution based on anomaly detection. Instead of attempting to list every possible defect, the system learns what a conforming part looks like and automatically identifies any significant deviation.

This approach offers several advantages:

• Reduced dependence on exhaustive defect libraries

• Ability to detect defects that are difficult to formalize

• Adaptation to natural variations in lighting or materials

• Stability across complex geometries

A multi-angle optical architecture, combined with lighting adapted to glossy or textured surfaces, complements the algorithm to ensure reliable detection in real production environments.

Beyond Detection: Leveraging Quality Data

Automating inspection is not enough. Understanding the root causes of non-conformities is essential.

The associated software platform enables analysis of production-line data: defect typology, frequency, drift by batch or machine. Quality teams gain concrete indicators to drive continuous improvement.

This holistic approach transforms cosmetic and packaging quality control into a true lever for industrial performance — beyond simple part sorting.

Ten Years of Field Expertise Supporting Manufacturers

Since its creation in 2016, Scortex has been deployed on production lines facing high aesthetic requirements: plastic packaging, metal or alloy components, decorated parts, surface treatments, premium components with strong perceived value.

This accumulated experience has refined:

• Use-case framing methodologies

• Optical integration strategies

• AI model training approaches

• Long-term performance monitoring

The objective remains constant: strengthen inspection reliability while relieving operators from repetitive and demanding tasks.

In a context where perceived quality is a key differentiator, securing every part leaving the production line becomes strategic. AI does not replace human expertise — it enhances it. And in the cosmetic and packaging sector, this synergy now makes the difference.Top of Form