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
- Meet Roman Eloshvili, Founder of ComplyControl
- Inside Mobile Payments with Bojoko’s Ville Saari
- Meet Steve Haskew, Group Director of Sustainability and Growth At Circular Computing
- A Chat with Hakob Astabatsyan, CEO and Co-Founder of Synthflow AI
- Meet Ernesto Suarez, CEO at Travel Insurance Provider: Gigasure
- Under Pressure and On the Clock: Gurhan Kiziloz’s Nexus International to Hit $1.45B Revenue in 2025
- Daisy Ip of InvestHK: Why Hong Kong Continues To Grow In Popularity Amongst UK Fintech Startups
- A Chat with AJ Balance, Chief Product Officer at Grindr
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.