Monolith empowers every engineer to exploit the benefits of artificial intelligence
Our proprietary machine learning software is available as a
private or public cloud solution.
Create your own
validate your data
Train your own Machine Learning Model
Analyse in real-time your input/output relationships
Your legacy database is an untapped goldmine …
Complex product engineering creates huge amounts of valuable data.
At the moment, 99% of it does not get reused.
Monolith is the only AI software built
to accurately capture physical relationships in engineering data.
Use your data to predict ahead in the design cycle:
How Monolith can help you
When can we reduce physical prototyping?
Make sophisticated predictions about your prototype
Most engineering products are not revolutionary, they are evolutionary improvements of an existing design. The more often you have built a product, and collected simulation or test data, the higher the likelihood that you can create a powerful machine learning assistant that helps you to better understand what would happen if you made certain changes to your design. Depending on how much data you have, you can either use Monolith for pre-design, design or even certification. Our customers have also found that creating an intelligent database of future engineering results helps to faster respond to customisation requests.
Predict ahead in the development process
The iterative nature of engineering means that over time it becomes possible to establish functional relationships between early-stage designs and late-stage performance. For example: by importing the simulation, wind tunnel and test data you have for the last five cars you have built, you can start building a model that predicts the performance of a new car. This makes the engineering process massively more efficient as engineers can forecast the effect of their decisions months ahead and thus avoid costly mistakes or delays.
Wind tunnel tests
How can we shorten test and simulation times?
Understand complex physics faster
Most product engineering application require huge amounts of testing in order to get a good understanding of the quality or performance of a design in a certain condition. It is important to get a clear understanding of how changes in geometric properties, usage conditions or manufacturing conditions affect performance.
Reduce the number of tests
Our Active Learning algorithms help engineers to reduce the number of tests needed to assess product performance, by suggesting the optimal conditions in which to carry out the next test.
Wind tunnel test
How can we learn from complex design data?
Parameterise CAD models with Monolith to be able to learn from them
In order to apply machine learning to product engineering, one often needs to include the 3D features of a product. Using parametric modelling is a useful in translating a set of numerical values into 3D geometry. However, when an series of CAD models are too complex to all be modelled with a single parametric model, our geometric DNA extraction algorithms have proved to be a sophisticated way to include such geometric data into AI predictions.
All our project engineers have extensive industry experience in advanced engineering. No need to worry about your application being too complicated or your data being in a difficult format - we understand. We’ll be there to help you figure out how to reimagine your company’s engineering all along the way.