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Empowering industrial engineers with AI

 

Monolith cloud-based AI software is built from the ground up for engineers with an intuitive, easy-to-use UI. It's designed for quick adoption and time to results for big data.​

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BMW
BAE Systems
Mercedes
Honda
Nissan
Siemens
Kautex-Textron
Michelin
Vertical Aerospace
Aptar
Jota
4 validation cases white paper battery testing
AI for simplifying validation testing 

Four applications for AI in validation test

 

AI has a significant impact on validation testing in engineering product development. You can reduce testing by up to 73% based on battery test research from Stanford, MIT, and Toyota Research Institute. Learn more with Monolith.

 

“Monolith allowed us to understand and optimise the gas meter's behaviour for all operating conditions and optimise meter accuracy under extreme conditions, allowing us to build a superior, more accurate product in a much shorter amount of time.” ​

Dr. Bas Kastelein, Sr. Director of Product Innovation, Honeywell Process Solutions

Packaging optimisation using AI with AptarGroup

 

In this exclusive customer webinar, Fabio di Memmo from AptarGroup & Monolith CEO Richard Ahlfeld talk about the value of Monolith’s intuitive AI platform for packaging, and how to accelerate decisions from months to minutes.

aptar case stusy
pharma case stusy

Optimising particle size and shape distribution from a target dissolution profile with Nanopharm

 

A random forest regression model was trained to predict the mass dissolved at different time points from the size and shape of the particles, to a good degree of accuracy. The resulting Monolith dashboards enabled users to upload a target dissolution profile and return an optimised particle size and shape distribution that would produce the target dissolution profile.

The next generation of smart meters using self-learning models

 

With Monolith, domain experts can easily investigate test data, combine and transform datasets, and build self-learning models—all within an intuitive AI platform. These models accurately predict flow rates across multiple material types and device configurations, such as valves with varying throughput characteristics, including radius, length, and other critical dimensions.

Common industrial engineering challenges

1

The time-to-market needs for industrial products and applications fail to meet customer expectations.

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2

Physical testing of every product iteration is expensive and time-consuming.

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3

Exploring and understanding a design space for all potential use cases is time-consuming and an inefficient use of resources.

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nanopharm case study pharma
Case study 

The next generation of pharmaceutical development using self-learning models

 

A random forest regression model was trained to predict the mass dissolved at different time points from the size and shape of the particles to a good degree of accuracy. The resulting Monolith dashboards enabled users to upload a target dissolution profile and return an optimised particle size and shape distribution that would produce the target dissolution profile.

 

webasto-2

There are many potential use cases for AI to speed up the battery test and validation process. After an extensive evaluation, we found Monolith to be an excellent option for scaling AI across our R&D.

-Markus Meiler, VP Research & Development, Webasto

Resources

 

Learn how Monolith helps industrial engineers test less and learn more

Other industries

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Automotive
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Aerospace and Defense
Aerospace and Defense
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