Monolith, a rapidly scaling Artificial Intelligence (AI) software company, is poised to radically reshape the development time of new cars. Its game-changing AI platform can substantially reduce testing and associated costs that automakers currently require to bring new vehicles to market.

Monolith software uses self-learning models to instantly predict the results of complex vehicle dynamics systems, reducing the need for physical tests or simulations. This game-changing approach will dramatically accelerate every stage of the automotive development process from initial design, design iterations, validation and production which currently require repetitive, time-intensive and costly tests and simulations. Using Monolith also results in fewer physical prototypes, travel to specialist test sites and on-road testing, making latter stages of validation safer and more sustainable.

- The Current Gap between Virtual and Physical Testing

To date, automotive companies use a combination of life-like virtual simulations and physical testing during vehicle development. For each design iteration, a simulation solves the physics that underpins the system’s modelling; a notoriously difficult and computationally intensive process. Virtual simulations help reduce the number of physical tests required, but the accuracy and fidelity of the results can be limited. Numerous physical tests are therefore still needed to calibrate and validate the virtual results, as well as to understand performance in operating conditions that cannot be simulated.

For example, aerodynamics optimises air flow over a vehicle to reduce drag and is notoriously difficult to solve mathematically which reduces the accuracy of simulated models. Owing to the highly iterative nature of the automotive design process, engineers supplement virtual aerodynamics testing with hundreds of hours of wind tunnel tests in facilities that can cost thousands per hour.

- Monolith is Transforming Automotive Product Development

Monolith offers an alternative and radical solution to reduce the time and cost of vehicle testing. Virtual and physical tests create significant volumes of valuable data that is presently underutilised. Now, with Monolith, this data can be leveraged to train highly accurate AI self-learning models to instantly predict the performance of systems by understanding their behaviour from data, instead of solving the complex physics of the system, or performing a physical test. Using this approach, engineers can rapidly predict performance in more operating conditions and for areas of the car that were previously impossible to simulate, further reducing the amount of testing required. Monolith is already being used to reduce wind tunnel, track, wheel and tyre, and vehicle dynamics, durability, crash and powertrain testing.

Dr Richard Ahlfield, CEO and Founder of Monolith, “Monolith was founded to empower engineers with AI to instantly solve even their most intractable physics problems. We know this resonates especially with automotive engineers who struggle to optimise hundreds of often conflicting criteria with hundreds of complex simulations. Requiring hours or days to solve, engineers have grown frustrated by the considerable amount of physical testing still required to make up for the limitations of the virtual tests. At the same time, the data that is created in the process represents an enormous opportunity when used with AI. By predicting results with self-learning models we can radically accelerate the development process.

Today, automotive companies are spending billions developing electrical architectures and software capabilities as they strive to win the race for electric, shared and autonomous mobility. This squeezes R&D budgets and product timelines in other areas, creating enormous pressure on the engineering teams working to develop higher quality vehicle hardware systems in less time and with fewer resources. As Akio Toyoda, CEO of Toyota put it, “data is the new gold” but the “[vehicle] platform will be the backbone for mobility as a service for autonomy, for car sharing, for any number of services that we want to make possible”. Data to make better vehicles whilst cutting costs and saving time - this is at the heart of how Monolith is uniquely transforming vehicle development.

The Monolith platform empowers automotive R&D teams to use AI to learn the best possible insights from years of existing test data, or instantly predict results from a small sample of current tests. Ultimately this means OEMs can bring new vehicles to market faster, which isn’t just vital to reach EV ambitions, but allows automotive engineers to do what they love best - engineering incredible new vehicles."

- Mature and Proven Technology, Ready to Scale

Monolith has spent the past six years developing its platform and working closely with some of the world’s top engineering teams to stress test it. Today it boasts a mature and proven technology that is being seamlessly integrated into customers’ day to day activities. Engineering teams at leading automotive OEM and tier 1 suppliers around the world are already realising substantial reductions in physical testing after working with Monolith:

1) Sensor and instrument company Kistler achieved a 72% reduction in sensor based testing

2) Honda recorded an 83% faster design cycle

3) JOTA Sports Endurance Racing Team reduced the number of simulations and tests by 50%, and associated costs by 66%

Dr Joel Henry, Principal Engineer at Monolith, said, “Optimising a system, or finding a new solution based on a decade of historical data, is like instantly offering an engineer a decade of experience. That’s the power of AI - it supercharges an individual’s subject matter expertise by unlocking the expertise stored within a company’s data. Monolith really is the engineer’s perfect partner.”

Built from the ground up by engineers for engineers, the no-code platform offers a seamless user experience with powerful interactive dashboards. The Monolith team is made up of industry and software experts who work with customers to identify their most effective use cases that can rapidly realise the value of AI.

Use cases are dependent on the needs of the business and the type of data. For example, an OEM can use its legacy data to find new insights hidden within its decades of expertise and unique data. Alternatively, data captured from a handful of tests using a physical prototype can be used to teach Monolith self-learning models to predict behaviour over more operating conditions; including under non-steady states, when variables of interest have not settled and are still changing over time. Monolith self-learning models predict behaviour under these typically difficult-to-capture non-steady states in a matter of seconds, instead of weeks or months capturing behaviour in all driving and operating conditions. This enables engineers to explore even more parameters and requirements to make products that are even more fit for purpose whilst substantially reducing development time.

- The $46 billion Opportunity

The business is currently focused on automotive customers but has ambitions and applications in innumerate industries. Monolith can be used for any system which requires data, repetitive testing or Digital Twins for design development, validation, production or data evaluation. Digital Twins, which are real-time virtual representations of a physical object or process, are increasingly used in a wide range of industries including manufacturing, healthcare, supply chain and retail. The digital twin market is estimated to be worth $46.08 billion by 2026. Monolith is already working in this space with global brands such as L’Oreal and pharmaceutical company Nanopharm.

Monolith is poised to swiftly scale with an expert team, network of industry partners, extensive portfolio of IP and £10.6M of funding.