Industry

Empowering Industrial experts 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.​

Trusted by industry experts :

L'oréal
Aptar
Nanopharm
BASF
Honeywell
Siemens

“…The most common mistake people make is that they hire data scientists without bringing the subject matter experts along. Successful application of AI is a marriage of data and expertise right down to the granular level.” ​

- Vincent Higgins, Global Director Digital Transformation, Honeywell

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.

aptar case stusy
pharma case stusy
Optimizing 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.

smart meters use case v2

Common Industrial engineering challenges

1

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

2

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

3

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

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.

Talk to an expert

Ben

Account Manager

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.

Ben

Ready to get started?

BAE Systems
Jota
Aptar
Mercedes
Honda
Siemens