Problem Solver

The new standard in

Root Cause Analysis

AI-powered software for root cause analysis

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Stop guessing, start improving

Your quality improvement is only as good as the analysis that triggered it. Our Problem Solver software supports your process engineers in identifying causes for quality losses amongst thousands of process parameters. Instead of reacting to quality losses, our AI tools enable you to prevent them from happening in the first place.


No Code

Unlock the benefit of cutting-edge AI without requiring coding skills; your process experts can focus on gaining insights and devising process improvements for your production lines.

AI Process Model

Use state-of-the-art algorithms to imitate physical processes. Our AI tools identify hidden and complex interaction effects that you were not aware of.


Speed up knowledge sharing and collaborative problem solving. Work on analysis results and simulations using living documents rather than screenshots or slides.


Root cause analysis has never been easier


Quality Control

Our Problem Solver software uses the power of explainable AI & causal algorithms to find needles in the haystack of your production data. The software can interpret a high variety of data formats to identify the hidden root causes of your quality losses. Your quality output may be represented as product images (for example, surface damages), categorical variables (for example, pass/fail), or numerical variables (for example, yield).

At a glance

  • Let your experts focus on the deep and harder questions.
  • Interprets a wide range of quality parameters (images, categorical variables, numerical measurements).


Data Streams

The Problem Solver ingests and sanitizes data from multiple production lines, no matter the format of your process and quality measurements. You can quickly get started using individually uploaded spreadsheets, and later build out real-time integrations via our factory-to-cloud API. Our experts provide hands-on support for your journey to full data transparency across all your factories.

At a glance

  • We quickly integrate any data format you may be using.
  • No need to first build a complex data pipeline. Gain insights right away.


Root Cause Algorithms

The Problem Solver helps you define the most promising actions for improving your production. At its core are cutting-edge methods from explainable AI, which our team of machine learning experts has tailored to real-world factory use cases. It quickly identifies quality-critical parameters and their interactions, and surfaces the non-linear and subtle dependencies that conventional methods fail to find.

At a glance

  • Quickly produces stack ranks of factors impacting your production quality.
  • AI process model imitates the relationship between quality and process parameters.
  • Uses the latest AI methods to tractably deal with thousands of interacting parameters.



Visualize your findings in a way that will convince all stakeholders to move forward. Easily share your insights with others, directly inside the software. This boosts teamwork and frees your process experts and data scientists to focus on where they provide the most value. The built-in charts and workflows guide your understanding of the physical processes in your production lines. Characterize commonalities and differences at the level of batches, lines, and factories. So you can quickly replicate what works, and avoid what doesn’t.

At a glance

  • The Problem Solver will give you well-informed and unbiased directions on your next steps.
  • Analysis result are statistically founded, and visualized in a clear and understandable form.
  • Build a shared understanding of quality problems, and turn insights into action.

Selected Use Cases

Leading manufacturers trust our software



  • Yield improvement in semiconductor production.
  • Identifying quality drivers from thousands of parameters.
  • Reducing yield losses by more than 50%.

Food Production

  • Yield improvement in food production.
  • Identifying quality drivers from hundreds of parameters.
  • Reducing yield losses for high-runner products.


  • Yield improvement in medicine production.
  • Combining offline measurements and time series data.
  • Reducing variation in product quality.