Along with new technologies, innovative academic and professional disciplines are solving problems in the cosmetic industry. One problem in particular cries out: how exactly do you sell cosmetics online? Or, to be more exact, how can you get customers to trust that your foundation, concealer, or skincare product is going to be a good match for them if they never get a chance to try it on? A product that looks like a perfect match to your skin on an iPhone screen might look very different in person

What this means is that most online makeup and skin care sales are conservative. Customers usually only buy products that they’ve already bought before – that they already know are a good match for them. But what happens when you need a new skincare routine, want to try the latest KVD foundation, or your go-to brand stops being sold? Tejo, a Canadian startup, is developing a comprehensive cosmetics recommendation engine in order to tackle this problem. It applies new image-analysis technologies to read a customer’s photo, determine the tone and condition of their skin, and generate suitable product recommendations.

Tejo’s founder, Rachel Baker, got her start in Industrial Engineering at the University of Toronto. Industrial engineering applies engineering techniques to business logistics and marketing—an approach that she argues is long overdue in the cosmetics industry. “I think the industry is being held back,” she remarked. “People are so used to high-pressure sales tactics, as well as influencer-based marketing and other stealth-advertising. We want to give cosmetic-buyers useful information, and cosmetic sellers useful tools. I think people are ready to trust the science of image analysis.”

Tejo intends to fully launch later this year, and future plans include expanding into hair-care and body-care.

Find Tejo’s Website Here: www://