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Atolla brings personalization to skin care

Atolla is an MIT-based startup that leverages machine learning to deliver customized skincare products using an individual’s actual skin data. They have developed a skin and ingredient database to match people with their most efficacious formulations and create products that evolve as a person’s skin changes with the seasons, environment and age. I sat down to speak with Meghan Maupin, CEO of Atolla and a student in the MIT Integrated Design + Management program.

Finding the right skincare products for YOU is currently an opaque process

When you walk down the skincare aisles of CVS or Sephora, you will find hundreds of products along the shelves with statements that advertise boldly, “visible wrinkle results start day 1,” “hydrates skin better than ten of the top creams”, or “improves skin texture.” The validity behind these statements is questionable. It is uncertain how these impersonal products you’re deciding between will help you solve your skincare needs. It is difficult to know how your skin needs might change with time and connect skin attributes to ingredients. One size doesn’t fit all.

As you’re probably already aware, personalization is key for millennials. Atolla identified a gap here: a need for personalized skin products based off actual dermal skin data and visual quantification of skin issues that does not have a self reported bias. The team is looking to build “a future where skincare is made for the individuals”.

Where it all began

In the fall of 2017, Meghan Maupin (CEO), Nava Haghighi (CPO), and Sid Salvi (COO) connected at MIT over a problem they all shared that affected their day-to-day: they had a hard time finding skincare that worked with their skin sensitivities (like the 30% of Americans who also have had an allergic reaction to skincare products). Meghan notes that when she would walk into a skincare store, she would find herself having no idea what product to choose from and uncertain about what would work for her concern. Having worked in the design lab at MIT on an AI project and in user-experience at Patagonia, Meghan thought this skincare product problem is something that could potentially be solved with data, especially with the measurement of product efficacy. Meghan connected with Nava, also a designer who has studied the physical future of retail at MIT, and Sid Salvi, a former consumer and retail strategist, who both shared a similar vision. The team has put together a team of advisors, including experts in the fields of dermatology, machine learning, skin science, cosmetic chemistry and chemical safety, who are helping the team bring technology to the skincare industry.

The team at Atolla (Meghan Maupin, Nava Haghighi, Sid Salvi)

The future of skincare

With a team strong in terms of backgrounds in data analytics and user experience, this is a competitive advantage in an industry that has historically ignored these two things. Atolla is currently using a physical skin test with a skin analyzer tool used by dermatologists, combined with environmental and lifestyle data shared via survey to measure a variety of factors including skin hydration, oil content, sun damage, age, and skin concerns and goals (such as, reduce fine lines or eliminate breakouts). These data points are then put in Atolla’s proprietary algorithm to recommend a custom facial oil formulation, down to the ingredient and dosage level that address the individual’s specific skin concerns. The team then prepares the facial oil right in front of you. All of this is done in under 10 minutes. Atolla can measure if the formulation is working over time and can make adjustments to the product based off changing customer needs like moving locations, changing hormones, or seasonal skin changes. The data is all tracked in a skin profile so that clients can keep track of changes over time. They are currently developing an app that uses Computer Vision to give the skin factors (ex. oil content and hydration) a relative score, and measure the results of how well a product is working. The team is also looking to eventually expand the line beyond facial oils to other personalized skincare products.

In terms of competitors in the space, there are some survey based skin products, however there are very few companies working with the physical data combined with the survey and machine vision scoring of skin concerns. This combination of data collection allows Atolla to measure the effectiveness of their product, helping the company develop a long-term relationship with their clients. Atolla believes the in-person data component is essential because consumers don’t necessarily know or understand the full picture of their skin (for example, the oil content of the skin). This allows the team to be precise and not biased in terms of their formulation because it is not all self-reported data. One challenge the team is currently working through, however, is the distribution channel that will be most effective—they could go straight to the consumer or partner with dermatologists, or both.

Custom Atolla Facial Oil

Next steps

Atolla is still in their beta phase, hosting a series of pop-up events to collect more data to improve the algorithm. This summer, the Atolla team will be in New York City, working on the launch of their Skin Profile apps and hosting a month-long pop-up event.

If you’re interested in attending an Atolla pop-up event or looking to learn more about the company, visit

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