Industrial leaders partner with us

IconPro AI Solutions are trusted by

Solve the Challenges

Quality problems and inefficiencies are difficult to manage, especially when processes change dynamically under many influences.

Unlock the Potential

Empower your production to predict & prevent losses with our ready-to-use industrial AI solution.
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Know IF losses will occur

Get real-time predictions about scrap including
prediction confidences.

Know WHY losses occur

Get to know the most relevant process influences on
scrap automatically and as a report.

Know HOW to prevent losses

Get to know how to prevent scrap by optimized
process parameter recommendations.


From data to information

Quality Data
Process Data
Material Data
Sensor Data
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Process Engineers

Understand and improve
process influences

Quality Engineers

Get quality and scrap
predictions + root causes

Shopfloor Operators

Get and configure optimal
process parameters

IT Administrators

Monitor and control
software & resource usage

Predictive Quality & Yield Solutions

IconPro ARES empowers your team to predict & prevent losses.
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Process Engineers

Understand & Improve –
Complex Processes

Visualize the directional influence and importance of every single process parameter, create and share reports and derive improvements.

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Quality Engineers

Predict & Find –
Scrap & Root Causes

Know about the quality of a part already before inspection and find hidden root causes for quality issues. Make quality assurance & control efficient.

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Shop Floor Operators

See & Configure –
Optimal Process Parameters

Continuously get recommended configurations for your process – live and taking into account any process changes. Increased reliability and repeatability.

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IT administrators

Monitor & Control –
Software & Resource Usage

Always know and administrate who is using the software with which resources. Scale the software internally easily, securely and stably.


Trusted by

Mechanical – Wire Eroding
Mechanical – Wire Eroding

Project Subtitle
Data-based evaluation of the wire electrical discharge machining process

Companies & Partners
WBA Aachener Werkzeugbau Akademie GmbH, Mak…

Automotive Metal Forming
Automotive Metal Forming

Project Subtitle
Industrial Reinforcement Learning for the Quality Control of Metal Forming Processes

Companies & Partners
Mubea, Tailor Rolled Blanks Gmb…

Manufacturing – Ultrasonic Inspection
Manufacturing – Ultrasonic Inspection

Project Subtitle
Quality Control of Vehicle Assembly using an Ultrasonic Imaging Sensor with Embedded Artificial Intelligence

Companies and Partners

Minimize scrap rates and maximize quality of your production line.

Enter your Parameters
IconPro AutoML helps to realize savings of dozens of thousands of euros per year per production line. Process optimizations are derived from analyses or can even be integrated into the processes.

Yearly Scrap Costs of Production Line

1 k€

  • 1
  • 200

Scrap Rate


  • 0.1
  • 30
  • Minimize your Scrape Rate to

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    2 %

  • Realize Annual Savings of

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    2 k€

How to start?

We will guide you through 6 systematic steps in less than 6 months. Get a reliable proof-of-concept in the most cost-efficient way!
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  • Understand your use case and requirements

  • Know if your current data is suitable

  • Collect & structure meaningful data

  • Extract the maximum data potential

  • Know the feasibility for your use case

  • Know the return on investment

  • Get roll-out roadmap after proof of concept


Our Offering

Make it as easy, reliable and cost-efficient for you as possible.
We cover the full range of services.
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– AI in Manufacturing

Learn more about AI in Manufacturing and its use-cases.

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Our team will be happy to help you. Get in touch!



Poor quality is expensive and hardly forgotten.

Conversely, good quality is when the customer returns, not the product. But how can manufacturing companies consistently maintain quality and reduce scrap?

Predictive quality, also known as AI quality assurance, is a data-driven strategy that strives to guarantee the quality of production processes and the final product.



With predictive quality, quality-related processes and products are continuously improved through data-based projections, from production scheduling to anticipated rejection.

To create predictions regarding future quality – and, if required, to start improving it – relevant elements are taken into account.

Predictive quality (AI Quality assurance) reveals patterns and correlations between quality data and production data that were previously unnoticed.

These results are utilized to develop prediction models using artificial intelligence (AI) and machine learning that predict product quality and suggest process parameter corrections.



Whether by a sensor, quality inspection measures, or customer feedback documentation, quality data for predictive quality models are often accessible.

On the other hand, the quality of a product is affected by a wide range of distinct production process variables.

Problems with the product can even arise even within the single-part production’s set tolerance limitations.

In AI quality assurance or predictive quality, all manufacturing parameter values ​​are automatically tracked and used to predict the potential product quality.

Corrections can be made before completion, thus reducing scrap and helping the process stability and capability.

Production data and quality data is used to train algorithms in AI software, which subsequently generates predictions automatically.

High levels of computer power are needed for the data-intensive procedure.

For instance, scalable cloud infrastructure can offer the required capacity. Standard interfaces can be used to make predictions and corrections accessible.

Machine learning techniques are used in industrial AI to identify quality deterioration or deviations from the norm.

Process experts are essential for confirming the validity of the data analysis results in the context of predicting and improving quality.

IconPro AI software continuously modifies the AI ​​models and algorithms depending on the findings of earlier analyzes through repeated feedback loops.

Furthermore, it must be defined in advance exactly what data is necessary, how it must be processed, and what level of forecasting accuracy is needed to provide actionable judgments.



A producer of automotive lighting seeks to reduce production scrap and improve process stability and capability.

The best process parameters may be found by training and using predictive models to improve the prediction quality.

IconPro analyzes information about the goods and procedures on the assembly line, which is generally accessible through the manufacturing execution system (MES).

A predictive quality model is trained using the MES data collected across the production chain.

Finally, already at early process steps within the production chain, the model offers quality predictions and suggests process parameter corrections concerning individual parts in-process.

The primary influencing factors or process parameters, respectively, are determined for each sample, thus making predictions and corrections transparent and understandable for the user.



The precise strategy (process optimization, root cause analysis, or scrap forecasting) will rely on each firm’s data situation and unique objectives.

 However, the benefits of predictive quality may be summed up as follows:

  • less rework and scrap
  • better quality and process capability
  • improved process stability
  • Less waste of materials and energy
  • enhanced planning reliability
  • Reduced throughput times
  • increased customer satisfaction



Customers are more often satisfied with products of obviously higher quality.

AI quality assurance or predictive quality uses machine learning to automatically decrease scrap, rejections, resource consumption, and rework in production while improving the product quality.

IconPro AI software is tailored to specific corporate conditions, depending on the IT infrastructure, data situation and use-case objectives. Through industrial AI, significant value can be added.