Data meets dermatology at Brooklyn-based skincare start-up Atolla, launched by an MIT engineer who has applied machine-learning algorithms to fix problem skin.
The Atolla Skin Health System works by giving users testing kits to measure the exact characteristics of their skin — specifically, its hydration, oil, pH and absorption.
It then sends them a face serum calibrated precisely to their skin’s needs, with the formula updated monthly.
Atolla’s co-founder Meghan Maupin — who has previously worked on an AI-powered personal assistant and products for mass customisation in 3D printing — was at the Massachusetts Institute of Technology when she decided to see if she could use her software expertise to improve her skin.
“I have super sensitive skin, and when I got to MIT, I found myself dealing with a whole new set of skin issues,” said Maupin. “I couldn’t understand what was causing my skin freakouts or how to solve them.”
“Challenged to figure it out, I realised that I was at the best place in the world to solve this complicated problem with machine learning.”
She teamed up with data scientist Sid Salvi, who was doing his MBA at MIT Sloan at the time, and dermatologist Ranella Hirsch to co-found Atolla in 2017.
They went to develop the idea at the MIT Delta V summer accelerator in New York City and launch a successfully funded Kickstarter campaign the following year. The company is now up and running, and sending out its unique skin health kits in the USA.
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