After years of research at world-leading universities (including Cambridge, Stanford, and Imperial College) and collaboration projects with some of the world’s most advanced engineering companies (including NASA, Rolls-Royce and Airbus) Monolith was born.
Our AI platform utilises the latest in machine learning, to help design and engineering teams significantly improve the product development process.
How did you come up with the idea for the company?
Monolith AI was founded from my PhD at Imperial College London and my research work on surrogate models of space launches for NASA’s Space Launch System (which will carry humans to Mars in the mid-2030s).
I have always wanted to build world-transforming tech with the vision behind Monolith being JARVIS from Iron Man, the ultimate assistant for engineers – imagine how quickly we could develop new products with that kind of help!
More from Interviews
- A Chat With Lucid Software’s Jamie Lyon: Why Shared Context Is Becoming AI’s Real Competitive Edge
- A Chat With Tomas Milar: What Startups Should Look For In A Cap Table Management Platform
- A Chat With Danny Sullivan, CEO Of Nomada Digital And PR54 Judge
- Dear Mr. Prime Minister: Mike Bray, VP, Technical Solutions At RS Group
- A Chat With Sisi Ncube, Founder Of FibreTempo, On Building Health Habits That Actually Stick
- A Conversation With Paul Dawalibi, CEO Of Innovation City
- A Chat With Anthony Clements, Head Of Brand Partnerships At Impact On How AI Is Reshaping Affiliate Marketing, Brand Discovery And The Customer Journey
- A Chat With Lisa Miles-Heal, CEO Of Corlytics, On What It Actually Takes To Build A Business Buyers Want
How has the company evolved during the pandemic?
The pandemic lockdown accelerated the steady growth of virtual and cloud engineering. Some of Monolith’s users are engineers at Kistler.
Testing new design performance in-person generally requires multiple people in a shared space. Therefore, throughout the global pandemic, these kinds of tests have either been impossible (depending on the extent of local restrictions) or meant risking your health. With Monolith, however, the engineers at Kistler can use data from previous tests they conducted and accurately predict what would happen this time.
We’ve also introduced a new teams feature – just like with Zoom or Miro – allowing different engineering teams to share data and dashboards and work together more collaboratively.
What can we hope to see from Monolith in the future?
Similar to how Google Deepmind delivered a breakthrough in protein folding, we want to deliver breakthroughs in engineering and sustainability. We’ve already started working with packaging manufacturers and retailers to create more sustainable products without compromising the customer experience. We are also currently focussing on larger projects, such as helping to develop hydrogen-powered aircrafts.
