Compare Jinni vs. Qloo

Jinni's innovative platform, focused on matching content to audiences based on taste, is transforming how video content is discovered and how targeted digital advertising is executed for entertainment brands. Utilizing its proprietary Entertainment Genome™, which encompasses thousands of unique content attributes or "genes," Jinni excels at discerning the nuanced distinctions in TV shows and movies while also recognizing the individual preferences of each user, leading to an ideal alignment between viewers and content offerings. Our goal is to establish ourselves as the premier content-to-audience platform for entertainment brands, effectively using a single system to connect and promote entertainment titles to the most suitable audiences, significantly enhancing profitability for both platform operators and advertisers in the entertainment sector. The semantic algorithms developed by Jinni that link user preferences to relevant content are paving the way for the future of content discovery and recommendations within the industry. By continuously refining these algorithms, we aim to elevate the user experience and drive engagement to unprecedented levels.

Qloo, the "Cultural AI", is capable of decoding and forecasting consumer tastes around the world. Privacy-first API that predicts global consumer preferences, catalogs hundreds of million of cultural entities, and is privacy-first. Our API provides contextualized personalization and insight based on deep understanding of consumer behavior. We have access to more than 575,000,000 people, places, and things. Our technology allows you to see beyond trends and discover the connections that underlie people's tastes in their world. Our vast library includes entities such as brands, music, film and fashion. We also have information about notable people. Results are delivered in milliseconds. They can be weighted with factors like regionalization and real time popularity. Companies who want to use best-in-class data to enhance their customer experiences. Our flagship recommendation API provides results based on demographics and preferences, cultural entities, metadata, geolocational factors, and metadata.