Use cases

Most engineering products are not revolutionary, they are evolutionary improvements of an existing design. The more often you have built a product, and collected test data, the higher the likelihood that you can create a powerful machine learning assistant that helps you to better understand what would happen if you made certain changes to your design.

70% reduction in track tests

Automotive manufacturers can see up to 70% track test reduction needed to assess the dynamic behaviour of vehicles, by optimising test campaigns.
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6 weeks shorter testing

Manufacturing engineers can save up to 6 weeks of track testing by combining learnings with wind tunnel data.
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Provide client quotes faster

Using Monolith AI has helped packaging manufacturers cut their Request for Quotation time from weeks to days.
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How it works

Import your data

Monolith learns from your data of previous design cycles. Import your time-series data recorded from sensors, 3D CAD files of tested prototypes, or tabular data from a test bench, and import it into Monolith.


Data Cleaning

The data might need more exploration to remove redundant or erroneous values. Monolith automatically processes this data to ensure it is in the optimum format for our advanced machine learning models.

Virtual testing & predictions

Whether it's compliance testing, design validation, new product, or a new testing condition. You can now predict the quality and performance to reduce testing.

If you change the design, Monolith will tell you how your new design will perform. Reducing the need for new tests and providing a quicker approach to product certification.