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Taste intelligence and AI vendor raises $25 million

Qloo raised $25 million in series C funding. Using machine learning, the vendor helps customers predict consumer tastes and makes it actionable through an API.

Cultural AI startup Qloo on Wednesday revealed that it secured $25 million in series C funding.

The funding round was led by AI Ventures, with participation from Moderne Ventures, and brings Qloo's funding to $57 million since its founding.

The New York City-based AI vendor specializes in decoding and predicting consumer tastes -- the aspect that is cultural -- and making them available to users through an API. Using machine learning and theoretical research in neuroaesthetics -- a new scientific discipline focusing on the brain's response to art -- Qloo helps enterprise customers drive sales, choose real estate locations and build brands.

The platform uses proprietary deep learning models that are built on consumer tastes and entity intelligence. The models are complementary to large language models because the Qloo models use behavior and geolocation to provide insight, according to the vendor.

Moderne Ventures and Qloo

Moderne Ventures, a venture capital fund that invests in technologies involving real estate and services in fintech and insurance tech, was interested in Qloo because the AI startup's technology applies to other verticals as well, partner Liza Benson said.

"What we love about Qloo is that they have a use case in real estate, but are not entirely dependent on real estate," Benson said.

While Moderne Ventures does not only invest in AI startups, the venture firm found Qloo's AI recommendation engine helpful for real estate owners with large retail operations looking for what types of stores pair well together, Benson added.

"We haven't seen anything with such a wide breadth of data, across people, places and things, with a geolocation component, as Qloo," she said.

Qloo's data source comes from proprietary entity data, first-party data from its TasteDive company and third-party data from sources such as Instagram, Twitter, Google Places and Goodreads.

Diagram of how Qloo's Taste AI uses anonymized data inputs to determine consumer preferences.
Qloo uses anonymized consumer data to provide personalized output.

Taste-matching AI

Qloo's ability to use its breadth of data to provide personalized results while removing personally identifiable information appealed to Michelin Guide, a series of guidebooks for restaurants and hotels.

Michelin's relationship with Qloo started in 2018 after Michelin acquired travel curator company Tablet Hotels, according to Michael Davis, Tablet co-founder and current Michelin chief product officer.

"We were already engaged with Qloo, but because we merged with a company that was also experiential, but on the culinary side, the gastronomy side, everything that we focused on Qloo is also applicable there," Davis said.

Tablet Hotels used Qloo's AI engine to curate a list of recommendations based on the traveler's preferences and various hotel attributes. Similarly, Qloo's AI recommendation engine curates a list of restaurants for users based on different attributes of the Michelin Guide.

It's about matching people up with the things that they share.
Michael DavisChief product officer, Michelin Guide

"It's really about taste matching," Davis said. "It's about matching people up with the things that they share."

For Davis, the interest in Qloo from investors comes from the AI vendor filling a gap that current generative AI systems such as ChatGPT are not addressing.

"The large language models are big, and Qloo is about the refining of the relevancy of taste," Davis added. "The taste matching is that level of personalization that large language models are not addressing because it's more about the mass interest by popularity or by frequency."

Data compliance

Moreover, Qloo's compliance with the European Union's GDPR and ability to protect personal identifying information appeals to investors and customers.

"We look to the highest standards of data privacy, and the European Union is certainly ahead of the U.S. on that," Moderne Ventures' Benson said.

For a company that does business in Europe and Asia, Michelin Guide has no problem complying with data privacy requirements through its use of Qloo, Davis said.

However, while Qloo currently differentiates itself with privacy protections, finding a balance between capturing data versus serving data to users' expectations will likely continue to be a challenge, he added. The AI vendor might also find it difficult to distinguish itself as large language models evolve.

"There will be a demand where you want more personalized results as there's more usage of the large language [models] because everyone's going to start having the same results over time," he said. "Their ability to fine-tune their results set is going to be an interesting development."

Vendors with similar recommendation products to Qloo include True Fit, Froomle and Rumo.

Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems.

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