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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Predictive Quality & Process Optimization
in Production

Get to know concrete user scenarios and added values through
Predictive Quality and Process Optimization in production.

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



What is the definition of Predictive Quality?

Predictive Quality is critical to any manufacturing operation because it helps ensure that products meet the desired quality and minimize waste.

Traditional quality assurance methods such as sample inspections or 100% inspections can be time-consuming, costly, and prone to human error. At the same time, they do little to identify causes of quality problems or even eliminate them in-process. For this reason, many companies are turning to Predictive Quality.

Predictive Quality uses machine learning algorithms to detect and prevent product defects before they occur. It uses data from a variety of sources, including material protocols, supplier documents, production equipment, and inspection process results, to identify patterns and predict the likelihood of defects or automatically optimize manufacturing parameters for the best possible product quality.


How does Predictive Quality work?

  • Data collection: The first step in implementing Predictive Quality is to collect data from various sources, such as machines, sensors, inspection processes, or customer feedback documents.
  • Data analysis: Once the data is collected, it is analyzed using machine learning. The algorithms look for patterns and relationships in the data.
  • Predictive modeling: Predictive models are developed based on the results of the data analysis. These models use the data to predict the likelihood of product failures.
  • Predictive monitoring: The predictive models are integrated into the manufacturing process, and production equipment is monitored in real time. If the predictive models indicate a high probability of product defects, this is indicated and appropriate action can be taken before scrap occurs or the product reaches the customer.
  • Process optimization: The predictive models are used to derive and provide optimization models. In-process and real-time suggestions for improving process parameters can be displayed and rejects can be reduced sustainably.


What are the benefits of Predictive Quality?

  • Improved product quality: Predictive Quality helps improve product quality. By predicting and preventing product defects, manufacturers can ensure that only high-quality products reach the market.
  • Reduced inspection effort: Predictive Quality enables dynamic random inspections by inspecting mainly products at the end of production for which a critical quality has been predicted, thus reducing inspection effort.
  • Lower process costs: Process optimization produces less scrap, which greatly reduces process costs through less rework, reduced material usage, and a higher yield rate.
  • Data-driven insights: Predictive Quality provides manufacturers with valuable insights into their production processes and even enables them to plan and realize higher quality in their current or future products.
  • Reduced recall risk: By predicting potentially defective products or even through realized process optimization, the likelihood of delivering defective products to customers, and thus recall risks and costs, decreases dramatically.
  • Reduced waste: By reducing the number of defective products, Predictive Quality helps conserve valuable resources and establish sustainable manufacturing.


What is an example of a Predictive Quality use-case?

For example, a manufacturer could use Predictive Quality to analyze data about its welding processes. The model would analyze data on parameters such as welding speed, temperature and pressure to detect patterns and anomalies. Suppose the model finds that a particular welding process repeatedly produces defective welds. In this case, it can alert the production team to investigate and fix the problem before it leads to significant scrap.