Industry

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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Get in touch with Monolith

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AI Readiness Scorecard

Complete Monolith's 3-minute assessment to develop an understanding of your organisation's readiness for AI, and which areas can benefit from the implementation of AI through an in-depth report sent directly to you.

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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.”

-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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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.

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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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Common Industrial engineering challenges

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

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

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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.

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Ben

Ben is a German-speaking science graduate who has spent over a decade working as an Account Manager at innovative software companies, supporting engineering organizations in understanding how adopting software solutions can accelerate and optimize engineering workflows.

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Resources

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

Other industries

Automotive

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Aerospace and Defense

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Industry

Empowering Industrial engineers with AI.