Blog Posts Raises $2.8M, Changing the Industry Standard with AI-Driven Product Recommendations

September 24, 2021 provides AI-driven, Amazon-quality product recommendations for any e-commerce store by using machine learning instead of user behavior data. We built to help e-commerce companies of all sizes utilize cutting edge AI and level the playing field against the anti-competitive forces of companies such as Amazon.

The Swedish company’s round was led by Initialized Capital, with participation from Y Combinator, EQT Ventures, Liquid 2 Ventures, Northzone, and a team of angels. recently launched with 21 customers, most of which have revenues exceeding $30M, including Staples.

“E-Commerce is booming, and recommendations are vital to e-commerce store success. Getting them wrong means lost revenue, and until now the best recommendation engines have been the domain of the e-commerce giants who have huge amounts of data and machine learning staff to dedicate to the problem,” said Brett Gibson, General Partner at Initialized Capital. “ fixes that by using cutting edge machine learning to give the rest of e-commerce the best possible recommendations at a fraction of the cost and integration complexity.”

“We love working with because of their super-strong AI and no-brainer 5-minute frontend integration,” said Magnus Jason, Commercial Manager Online, Staples. “Before, we put great efforts into manually curating our recommendations for our 23,000 products. Now we have zero manual work and a greater conversion rate on our site.”

For background, was started by recently turned 18-year-old Oliver Edholm. Oliver was the youngest AI researcher in the world when he began working as an AI research engineer at Europe’s leading payment company, Klarna, at age 15. He was inspired to start after seeing many SMB e-commerce sites trying to use a machine learning algorithm for product recommendations, but without sufficient data to train their models.’s product recommendation engine is unique in that it doesn’t require any user behavior data, yet still operates at the same level of quality as the giants in the e-commerce space, increasing sales 4-6%, and improving as the algorithms learn over time.

Existing recommendation systems require vast amounts of user behavior data to create recommendations, but this only makes recommendations accessible to large, already established e-commerce companies that have a lot of customers and historical data. Smaller SMBs have to accept imperfect recommendations for their customers, decreasing their bottom line, and losing market share against larger competitors.

How we do it has shifted the industry’s perspective in its approach to product recommendations. Others make machine learning models that are really good at understanding products by looking at the user behavior data around the products. creates machine learning models that are really good at understanding the actual products, the way humans do. does this by utilizing the most recent advances in deep learning, training deep neural networks to understand images and text behind products at a deeper level than anyone has done before.

Try it

We can integrate with any e-commerce platform in minutes so that potential customers can see the revenue impact of on their e-commerce stores almost instantly. Using’s extensive infrastructure, any e-commerce store will have a no-code way to compute their product recommendations and add them to their stores quickly, integrating through Google’s G Suite. Then, will automatically launch an A/B-test on the site, enabling potential customers to measure the recommendations’ revenue impact objectively. We’ve seen a 2x increase in click-through rates when A/B tested against AWS Personalize. If you are interested in testing, please contact us here.

One more thing, we’re hiring:

Software Engineer

Head of Sale

Full Stack Developer

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