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Industrial
Quality assurance with AI -
The future of manufacturing

Quality management
as a decisive success factor

Quality assurance (QA) carries out comprehensive measures aimed at achieving this, Defects in products and processes proactively and to avoid the Compliance legal and industry-specific Quality standards to ensure that The implementation of quality objectives through their Planning, documentation, monitoring and optimization along the entire value chain should ensure this. Supplemented by legal standards and norms from the Quality management (ISO 9001), quality assurance forms the decisive foundation for faultless products in your company. Manufacturingsustainable Competitiveness and maximum Customer satisfaction.

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With 36ZERO Vision this approach is being fundamentally expanded. Through the use of state-of-the-art Deep learning technology the visual Quality control is not only automated, but also becomes more intelligent and precise in its execution. While conventional systems often interpret every deviation as a defect, the AI-based solution from 36ZERO Vision recognizes the defect. Difference between genuine error patterns and harmless deviations. This eliminates so-called pseudo-errors, which makes the Efficiency of the review significantly. The AI analyzes production images in real time, clearly classifies identified error types and thus enables targeted control of subsequent production steps. Processes in production. This not only supports the continuous improvement of the production processes, but also helps to Compliance of defined quality characteristics reliably ensure. Another advantage is its flexibility: thanks to the no-code self-service platform, users can train the AI independently. The system can be adapted to new requirements with just a few sample images. Products, Variants or changing series. The models run independently on-premiseare hardware-independent and fit seamlessly into existing Quality management-methods. In this way, 36ZERO Vision creates a new form of Quality assurance - precise, efficient and future-proof. Companies benefit from fewer rejects, more stable Processes and one continuous Improving the quality standards that the Compliance of all relevant standards.

Boundaries of conventional quality assurance methods in your production

Traditional inspections detect anomalies based on machine learning and statistics, but cannot classify errors.

As a result, these technologies cause Pseudo errorrejects, unnecessary inspections and production delays.

Time-consuming manual (re-)checks drain resources and slow down processes.

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36ZERO Vision:
AI-based Quality Assurance
in industry.

AI-supported
image analysis in real time

  • Image-based deep learning models recognize actual error patterns.
  • Eliminates pseudo errors and increases the accuracy of quality assurance.
  • Artificial intelligence recognizes completeness and can read QR codes and plain text.
36zero Vision platform application 3 steps 01 Upload images

Testing and monitoring of error patterns

Types of error are categorized clearly and at an early stage, creating great added value:

  • Increased efficiency of production processes
  • Control of subsequent production steps
  • Traceability and containment in the event of recalls
  • Higher customer satisfaction in the long term

No-Code
Self-service
platform

  • Cloud platform: AI training directly by the user.
  • 5 - 25 sample images per fault type.
  • Effective adaptation to new products, variants or series through an iterative process.
36zero Vision Platform Application 3 Steps 03 Integration

On-Premise
execution

  • Local inference on existing systems.
  • Hardware-independent, compatible with all common industrial cameras.
  • Ideal for retrofitting and company-wide standardization.
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radically
in the context of
Quality management (ISO 9001)

36ZERO Vision complements existing quality management systems:

  • Quality planning:
    Definition of the relevant test characteristics and quality requirements
  • Quality control:
    AI-supported process monitoring and automatic corrections
  • Quality control:
    Objective analysis of known error patterns
  • Documentation:
    Automatic and transparent traceability through data archiving
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Automated quality inspection in production as competitive advantage

With 36ZERO Vision, visual inspection of products becomes a decisive success factor:
  • 1 // Best-in-class error detection: Data-centric, supervised AI model reliably classifies even complex error patterns and relieves employees.
  • 2 // No-code self-service: No programming or AI knowledge required.
  • 3 // Hardware-agnostic & scalable: Flexible use in new and existing infrastructure (greenfield & retrofit).
  • 4 // Fast & efficient: Typically only 5-25 training images and 2 hours effort for a productive model, analysis speed 20 ms. or faster.
  • 5 // Eliminate pseudo errors: Faultless products to reduce material and production costs and ensure efficient use of resources.
  • 6 // Transparency instead of a black box: Full access to the logic, customizable at any time at no additional cost.
  • 7 // Standardized interfaces: Smooth integration with renowned, world-leading partners.
  • 8 // Process added value: Fault pattern recognition provides valuable insights for the continuous improvement of product quality and production processes.
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The most important Questions
at a glance

How is AI-powered visual quality inspection transforming manufacturing from reactive defect control to proactive zero-defect production?

36ZERO Vision analyzes every inspection process not only for good/bad (iO/niO), but classifies error types, frequencies, and patterns in real-time, making this data usable for overarching process control. This error pattern data enables production managers and quality engineers to proactively identify recurring causes such as tool wear, machine setting drift, or material fluctuations, and adjust process parameters before larger amounts of scrap occur. While end-of-line inspections only capture defects after they have occurred, AI-powered inline inspection creates a continuous feedback loop into manufacturing control. The result: measurably more stable processes, lower scrap rates, and a systematic approach toward the goal of zero-defect manufacturing.

Why is AI-based visual quality inspection particularly robust under varying production conditions?

AI-based visual quality inspection is particularly robust under varying production conditions because deep learning algorithms learn defect patterns directly from real production data, rather than relying on rigid rules or predefined statistics. The model is therefore inherently adaptive to lighting fluctuations, changing material batches, new product variations, and seasonal temperature changes. Furthermore, 36ZERO Vision synthetically generates and provides possible production variations during model training, ensuring a robust model from the very first deployment.

Does AI-based visual quality inspection replace human quality inspectors, or does it support and relieve them?

36ZERO Vision is primarily designed as an automation and support tool, not a complete replacement for human quality inspectors. The platform takes over tedious, repetitive inspection tasks where human attention is known to wane and error detection rates drop – making consistent, objective decisions without fatigue effects, around the clock. Complex borderline decisions, context-dependent quality judgments, and continuous model improvement through human-in-the-loop feedback remain valuable tasks for quality professionals. The result is a hybrid quality assurance system that combines human expertise with machine consistency, speed, and seamless documentation.

How does AI-based visual quality inspection scale with frequent product changes, new variants, and cross-site rollouts?

Scalability is a core feature of the 36ZERO Vision Platform. AI models are trained via the no-code user interface with typically 5-20 training images—without programming and without external specialists. Additional products or variations then iteratively improve the respective model with 1-5 sample images. Trained models and data pipelines can be centrally managed at any time via the cloud platform and rolled out to any number of inspection stations, production lines, and international locations. This standardizes quality standards globally and unifies inspection decision logic across plants.

Is AI-based visual quality inspection economically viable even for small batch sizes and high-mix, low-volume manufacturing?

Yes. Especially in high-mix, low-volume (HMLV) manufacturing, 36ZERO Vision's platform offers particular economic advantages. The fast model setup with typically 5–20 training images makes even short series and individual production runs economical – the setup effort per variant change is reduced from weeks to hours. The model change itself is done with a mouse click; furthermore, the AI can select the appropriate model based on the part to be inspected. The typical payback period of 5 months has also been proven in medium-sized manufacturing companies with variable product mixes and high-mix production.

36ZERO Vision sets new standards based on industrial image processing and deep learning.

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