Monolith AI, a UK-based engineering software startup, secured £8.5M (approx €9.9M) in Series A funding, taking its total funding raised to £10.6M.
The startup builds new software that utilises the latest machine learning technologies to help engineers significantly improve the product development process.
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The round was led by New York-based global private equity and VC firm Insight Partners, with participation from existing investors Pentech and Touchstone. Other investors include Alejandro Agag, the founder of Formula E and Extreme E, Apex Black, and the Stanford Angels of the UK.
The capital will allow Monolith AI to grow their international client base and continue to develop their 3D machine learning technology that accelerates product development workflows by virtually predicting product performance.
Dr. Richard Ahlfeld, CEO and founder of Monolith AI, comments: “Deriving insight from data for decision-making is a complex but necessary challenge for engineering organisations. Engineers understand the data, they are embedded in the business and have the intuitions necessary to know why to process and model data. Democratising Al, by empowering engineers to be self-sufficient in building and deploying Al solutions, helps engineering organisations drive digital transformation more efficiently than other currently available solutions.”
New York-based Insight Partners is a private equity firm that invests in growth-stage technology and software companies. The VC has invested in more than 400 companies worldwide and has raised more than $30B in capital commitments through a series of funds.
The company is on a mission to find, fund and work successfully with visionary executives, providing them with practical, hands-on software expertise to foster long-term success.
Josh Fredberg, Managing Director at Insight Partners, will join the board. He says, “Whether we talk about aircraft, automotive, electronics or any engineered product, engineers make high impact decisions that drive revenue, cost, safety, and sustainability. Richard and the team at Monolith have created an incredible offering that allows engineers to learn from data from across the entire product development phase and make better decisions earlier in the process. We’re delighted to have the opportunity to work closely with the company as it continues to grow.”
Aims to democratise machine learning
Monolith was founded in 2016 by Dr. Richard Alhfeld after years of research at universities like Stanford University and Imperial College London and project collaborations with NASA and Rolls-Royce.
The company is on a mission to democratise machine learning for product development by building intuitive coding-free software, enabling engineers to understand, predict and optimise products efficiently using AI algorithms.
Currently, the company works with clients such as BMW, Honda, BAE Systems, and Siemens.
How does it work?
Monolith harnesses data from across the entire product engineering process, together with its advanced data science and AI models, to help engineers better understand what would happen if they make changes to design.
With Monolith’s solution, companies can optimise their R&D processes, increase agility, and reduce internal costs, delivering better products to market.
A few days back, JOTA, a British sports car racing team, partnered with Monolith AI to accelerate the design and engineering processes.
Monolith’s AI-powered technology will be used to optimise and validate track test-and-simulation data, empowering engineering teams to make better design decisions.
The technology will be an integral part of JOTA’s design-decision and track-testing capabilities, bringing new insight to an already distinguished engineering team, per the partners.
JOTA will use AI in every area of engineering – car setup, vehicle dynamics, aerodynamics, and tires.
Tomoki Takahashi, technical director at JOTA, says,“Monolith has already radically changed how we operate within the engineering team here at JOTA. Their technology empowers our engineers to make faster, better design decisions and streamlines how our car and simulation data are validated.”
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