A truly personalized, cross-domain recommendation engine that helps you discover your next favorite things.

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Cross Domain

Hai uses deep collaborative filtering to learn users preferences from across channel data-sets. The technology learns all the complex patterns driving tastes cross-domain, then offers recommendations tailored to those unique tastes on your very own platform.

Trust and Privacy

Each individual user's data is strictly private and secured within its own library. Therefore all of Hai's recommendations are given with greater accuracy and without ads, bias or financial interest.

Group Recommendation

Hai's powerful machine learning algorithms can generate unified recommendations across multiple users. Through the technology's merged taste profiles, Hai can find recommendations accross platforms for groups of people.

User-Trained

Hai empowers users to train their own algorithms. Hai provides them highly-customized recommendations based on how they interact with it. By focusing on the individuality of each person, Hai helps each person discover unforseen favorites that they may not even know about. Users can also merge their interests with other individuals to get group recommendations that all can enjoy, while maintaining their privacy.

Want to check it out?

Hai is currently in closed beta.

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