Empower your Automotive engineers with AI.

Spend less time running expensive, repetitive tests and more time learning from test data.

Monolith’s no-code AI platform empowers engineering domain experts across all facets of the Automotive industry.

Reduce expensive & labour-intensive testing.
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Decrease risks to product performance & quality.
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Shorten product development duration significantly.

Trusted by leading industry experts :


“With Monolith’s machine learning method, we not only solved the challenge, we also reduced design iteration times and prototyping and testing costs. We are thrilled with the results, and we are confident we have found a way to improve future design solutions.” ​

-Dr. Bernhardt Lüddecke, Director Validation Global at Kautex

Automotive use cases

Automotive Wind Tunnel Testing

Customers use Monolith to create a Digital Twin of the product to be tested and can manipulate the Digital Twin’s properties while in the wind tunnel​. By importing data from the sensors, test rig, and airflow, the customer can accurately predict readings from sensors that have failed, the aerodynamic performance of the product, and the impact of air contamination on the test​. The resulting reduction in wind tunnel time saves tens of thousands of pounds per day.​

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Vehicle Dynamics ai solution monolith
Vehicle Dynamics ​

Using Monolith’s intelligent exploration tools, the data can be sorted and easily visualized within the platform to inform engineers. Machine Learning models can be trained on certain manoeuvers, to predict the forces on the vehicle during manoeuvers that were not performed, thereby reducing total testing time. This allows for a test campaign to be optimized, reducing the number of tests performed to characterize the vehicle behavior.

Automotive Crash Testing

The Monolith platform allowed the customer’s engineering team to quickly import and process sensor data, then transform it and create machine learning models. ​Models created from a small initial number of tests and tests from previous vehicles enabled the engineers to predict the outcome of the regulatory tests. This enabled them to adjust the safety aspects of the design of new vehicles to satisfy regulatory tests much faster, with fewer resources and lower cost.​

Automotive Crash Testing machine learning platform
Engine calibration solution
Engine Calibration​

Our solution was to learn from previous tests and perform new virtual tests in the Monolith platform to quickly validate or invalidate options to reduce fuel consumption. In this use case, we looked at the effect of three design changes on the fuel consumption: 1) earlier combustion, 2) lower charging loss, and 3) use of an intercooler.

Combustion Chamber

“Combining simulation tools with artificial intelligence offers new design freedom to our customers,” said Jean-Claude Ercolanelli, Vice President of Simulation and Test Solutions, at Siemens Digital Industries Software. “With the ability to instantaneously explore new design options, while still satisfying target goals and constraints, AI extends the abilities of engineers to save time and unleashes innovation even without the benefit of CAD access.”

Combustion chamber optimization for automotive engineers

Key Automotive industry challenges


Wind tunnel tests are expensive, and almost every test is rushed towards the end of the shift. Self-learning models offer high-value insights into wind tunnel measurements, thereby improving test planning and execution. Test engineers can draw complex data dependencies between multiple design variables, without being constrained by the limits of traditional physics-based methods.


Track tests are a fun day out, but preparation for track days can be a headache. Failing sensors, loss of data, noisy data, and poor weather conditions can all jeopardize a successful track test outcome. Self-learning models can significantly enhance the value of track tests. For example, a transient track test is often sufficient to produce results that are otherwise obtained from an additional steady-state test. 


Crash tests are one of the most expensive destructive tests that every auto OEM must perform, as each requires building a new prototype car. Using self-learning models has enabled our customers to find a direct correlation between different crash scenarios and parameters linked to achieving a 5-star Euro NCAP rating.


Self-Learning Models for Complex Racing Systems

Improve Race Car Performance Using Intelligent Algorithms With Jota Sport

This webinar presents how test engineers can use Monolith to quickly parameterize and model complex tests and reduce testing time. Using the platform, they can optimize test cycles, predict results, and understand relationships and sensitivities between input parameters and their component/product performance, leveraging past test experience and datasets.

Talk to an expert


Technical Account Management

After a decade-long career with Siemens Digital Industry Software, Gaurav has embarked on a new journey in the world of artificial intelligence at Monolith. Over the years, Gaurav has helped engineers from automotive, aerospace & defense, marine, and other mechanical industries understand and solve complex challenges using both simulation and physical tests.

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Discover more AI resources for Automotive

Automotive engineering customers have reported up to a 70% reduction in track testing time, plus a 45% reduction in overall associated costs, while increasing the ROI of costly wind tunnel testing.

Other industries

Aerospace and Defense
Aerospace and Defense

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