How do you get started with an initial use case?

Start with a clearly defined inspection process and typical defect-free and defective parts. Building on this, a pilot can be quickly implemented on a production line.

How complex is the AI training process?

The models are trained using image data from your production process and refined step by step. Deployment often begins with a clearly defined defect pattern or a prioritized defect class before additional variants and defect types are added.

How are data and models kept under control?

Image data and AI models are stored in a secure, European cloud environment. You control which data is used and how long it is stored.

Visual Inspection

Rely on AI-powered visual inspection that automatically checks every unit in real time—right on your production line. This helps you protect your intellectual property, reduce scrap, and take the pressure off your quality assurance and operations teams.

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Automated visual quality inspection using AI image recognition on a factory conveyor belt.

Challenge: Visual Quality Control Under High Pressure

In many factories, quality assurance still relies on manual visual inspections in repetitive processes. This leads to fatigue-induced errors, inconsistent decisions, and quality variations that are difficult to trace.

Skilled Labor Shortage & Susceptibility to Errors

A heavy reliance on purely manual inspection, when performed repeatedly, leads to inconsistencies and misclassifications.

Lack of protection for sensitive data

Highly sensitive images of prototypes or new manufacturing processes run the risk of leaking into cloud environments without adequate data sovereignty.

Time pressure

Defects must be detected within milliseconds so that defective parts can be immediately removed without slowing down the production flow.

Increasing Customization

The growing variety of variants—for example, in software-defined products—requires flexible inspection processes that do not necessitate time-consuming reprogramming for every configuration.

Measurable Value: Quality, Costs, and OEE at a Glance

Use visual inspection on STACKIT to measurably improve quality processes. Transform spot checks into comprehensive inspections and create a robust foundation for quality and production decisions.

100% Inspection & Quality: Enhance product safety through automated quality control and reduce scrap and rework.
Cost Reduction & Increased Efficiency: Reduce manual QA efforts and avoid product recalls through early defect detection during the ramp-up phase.
Higher OEE Values: Minimize downtime through automated detection of anomalies in image and sensor data, and optimize cycle times through automated counting processes.
Compliance & Audit Assurance: Automatically document inspection results to meet requirements under TISAX, NIS2, and other standards.

Solution: AI-powered quality inspections on a robust infrastructure

Rely on specialized AI models (e.g., based on PyTorch) that inspect products, packaging, and components in real time and integrate them into your automation environment as automated quality controls. Packaging quality checks, product counting, and object detection automatically identify scratches, assembly errors, or missing components.

Latency-critical image analysis takes place directly on the shop floor, while analysis and model training run centrally via a Unified Analytics Platform. Using industry standards, you can integrate the solution with your PLC so that defective parts are automatically ejected.

This creates an edge-to-cloud quality control system—from the sensor to the cloud—with robust protection of your intellectual property on the STACKIT GPU infrastructure.

Competitive Advantage: Quality as a Differentiator

Use visual inspections to turn quality into a reliable competitive advantage. Increase transparency in production and shorten response times to quality issues.

Respond Faster

Identify patterns in defect images early on and implement corrective measures before quality issues spread to mass production.

Scale Flexibly

Scale new inspection setups and AI models in a standardized manner to additional production lines and plants—without lengthy project timelines.

Confidently protect IP

Store image data and models in a European cloud with clear data sovereignty.

Trust throughout the supply chain

Document your quality processes with digital evidence and strengthen the trust of customers and partners with high compliance requirements.

The Foundation: Edge-to-Cloud Architecture for Industrial AI

Build your visual inspection on an architecture that combines real-time capabilities on the shop floor with centralized AI management. Combine edge inference, a sovereign cloud infrastructure, and container-based platforms into an industrial AI environment.

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Edge Inference on the Shop Floor

Perform latency-critical image analysis directly on the production line to detect defects within milliseconds and automatically reject them.

STACKIT GPU Infrastructure

Train and run AI models securely on sovereign GPU resources in European STACKIT data centers.

Container-based AI Workloads

Deploy image processing and AI services using Docker containers and Kubernetes—from the edge to the cloud.

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Unified Analytics Platform

Train and optimize models centrally, analyze test and process data, and deploy updates in a controlled manner across production lines and plants.

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Industrial System Integration

Connect camera systems, edge devices, and AI services to your PLC environment as well as existing MES or QMS systems.

Frequently Asked Questions (FAQ)

Ready for 100% visual quality assurance on a sovereign infrastructure in your production facility?

Talk to our industry experts.