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The State of AI in Engineering

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Empowering Industrial engineers with AI.

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

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Trusted by industry experts :

L'oréal
Aptar
Nanopharm
BASF
Honeywell
Siemens
4 validation cases white paper battery testing
AI for simplifying validation testing 

 

4 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 optimize the gas meter's behaviour for all operating conditions and optimize 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 no-code AI platform for packaging, and how to accelerate decisions from months to minutes.

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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 optimized particle size and shape distribution that would produce the target dissolution profile.

The Next Generation of Smart Meters Using Self-learning Models

 

Using Monolith to investigate test data, users can combine, transform and build self-learning models inside our no-code AI platform that accurately predict flow rates for multiple material types, through devices such as valves with varying throughput capabilities such as radius, length, and other relevant device measurements.

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 a time-consuming and inefficient use of resources.

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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 optimized particle size and shape distribution that would produce the target dissolution profile.

Featured Content 

 

Robust active learning for next test recommendations

 

Integrating Monolith in your verification and validation process can enhance operational efficiency and streamline testing procedures, reducing reliance on excessive physical tests. 

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Learn how Monolith helps Industrial engineers test less, and learn more

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