Press Release

Engineers find data issues faster with deep-learning  ‘Anomaly Detector’ algorithm from Monolith 

Months of manual engineering time to detect data abnormalities is now practically immediate with AI

  • Monolith ‘Anomaly Detector’ automates the time-intensive process of inspecting raw test data for potential errors or abnormalities 
  • Failure to recognize issues within test data in a timely manner can lead to months of wasted testing and costly product delays and recalls 
  • Monolith developed and tested its anomaly detection algorithms in real-world applications with existing customers, predominantly in automotive 
  • With the Monolith Anomaly Detector, users can find a broad range of anomalies in isolated test results, or recognize abnormalities in cross-channel results based on complex system relationships  
  • Monolith CEO: “We’ve spent more than two years working directly with our customers to design, test, and tune our new Anomaly Detector so engineers can rapidly find errors in engineering data.” 
  • Join Monolith webinar on 9 April for deep dive into new AI-guided anomaly detection capabilities: 

4 April 2024 – Monolith, artificial intelligence (AI) software provider to the world’s most innovative engineering teams, has developed the industry’s first AI-powered ‘Anomaly Detector’ software that can discover a broad range of issues in test data at an unprecedented rate. It does this by automating the process of raw test data inspection to look for potential errors or abnormalities across hundreds of test channels. 


The impact of failing to recognize issues with test data in a timely manner can be huge, including months of wasted testing and potential product delays and recalls, leading to unnecessary costs ranging from millions to billions of dollars, studies have found. (*see notes to editors) 


Data anomalies caused by measurement or sensor errors, user errors, system malfunctions, or incorrect usage of the system during testing, can now be found quickly and more efficiently thanks to Monolith self-learning algorithms. 


Dr. Richard Ahlfeld, Monolith CEO and founder said: “Bad data leads to wrong decisions and massive time wasted among highly valuable engineering resources. If discovered too late, these errors can also lead to schedule delays, or worse, quality issues that are released with the product leading to potentially expensive and reputation-harming recalls.” 


“We’ve spent more than two years working directly with our customers to design, test, and tune our new Anomaly Detector so engineers can rapidly find errors in engineering data. In that time, we’ve not only developed a unique deep learning algorithm that can find multiple types of anomalies, we’ve also packaged it in a no-code user experience built specifically for engineering domain experts. This is useful AI developed by engineers for engineers.” 


24 months in real-world development with Monolith customers 

Monolith has developed and tested its new Anomaly Detector AI in real-world applications with existing customers, predominantly in automotive, motorsports and industrial segments. In working directly with customers, the Monolith team was able to create a unique deep learning algorithm that finds many types of anomalies within test results and across hundreds of channels based on complex system behaviour.  Users can tune the anomaly detector for speed or depth of inspection, as well as for prevalence or severity of anomalies. Using an intuitive two-dimensional heat-map display, engineers can quickly peruse the results and rapidly recognise which tests or channels are showing questionable results to prioritise next steps.  


Anomaly Detector is a new release in the Monolith platform of AI-powered tools, and follows the launch of ‘Next Test Recommender’ in 2023, software that gives active recommendations on the most valuable test conditions to validate during the development of hard-to-model, nonlinear products. 


The power of the Monolith platform lies in its ability to reduce the amount of physical testing time and simulations required to successfully develop products with highly complex, intractable physics throughout the design cycle. Using valuable and sometimes limited engineering test data, Monolith makes instant predictions and enables engineers to identify areas where optimisation and development are required, without the extensive need for repetitive, time-consuming physical tests. 


Monolith will host a webinar that takes a deep dive into its new Anomaly Detector technology on 9 April 2024. Follow the link to register. 


About Monolith:

Monolith software is trusted by the world’s top engineering teams including Siemens, Honeywell, and the BMW Group to develop better quality products in half the time. Backed by one of the world’s largest software investors and recognized by Gartner as a Cool Vendor for AI in Automotive, Monolith empowers engineering domain experts across vertical markets solving intractable physics to reduce expensive, time-intensive testing, lower risks to product performance and quality, and cut product development time. Featured in Forbes magazine and named one of the UK’s top 100 start-ups, Monolith was founded by Dr. Richard Ahlfeld, who received his PhD in Aerospace Engineering from Imperial College and was named to MIT Technology Review’s Top 10 Innovators under 35. Monolith is headquartered in London with global enterprise clients worldwide. 


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