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“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.” ​

Bas Kastelein
Sr. Director Product Innovation,
Honeywell Process Solutions

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Use cases

Packaging optimization 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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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.
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Key industry challenges


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


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


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

The Next Generation of Pharmaceutical Packaging 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.