Siri’s Sister Company Launches a Discovery Engine

Trapit Activity Feed

Trapit now has an activity feed to surface the most recent content.

Trapit, a startup that personalizes content with the same artificial intelligence technology that powers Apple’s Siri, is launching its public beta.

Trapit was unveiled in June as a system for personalizing content based on keywords, URLs and reading habits. The company describes itself as a “Pandora for content.” By using its A.I. technology, Trapit can identify related content based on contextual data from more than 50,000 sources.

Like Siri, Trapit was born from the $200 million CALO Project (Cognitive Assistant that Learns and Organizes). CALO was the largest artificial intelligence project in U.S. history. It was funded by DARPA (Defense Advanced Research Projects Agency), the Department of Defense’s research arm.

Since its private beta, Trapit has been working hard to revamp its discovery engine. The biggest change is its shift toward search. Trapit now prominently features a search bar on the top of the page. A search for a topic (e.g. “Space”) returns 100 results (“traps”) with articles and content related to that topic.

Clicking on a result returns a trap filled with content related to that article or piece of content. These traps get smarter as you click on articles and vote up or down content within them, improving their relevancy over time.

Trapit has also thought about the site’s usability. The company has added a real-time activity feed that makes it easier to find the newest articles within a specific search or trap.

“You need technology to discover [content] for you,” Trapit co-founder Gary Griffiths says.

The changes directly position it against Google, though the company claims that it isn’t in the business of search. “We don’t think of ourselves as a search engine,” says Trapit co-founder Hank Nothhaft says. “We do think the web is shifting from search to discovery.”

Nothhaft and co-founder Gary Griffiths say “Google is like the yellow pages.” It is best suited for finding exact things like a replacement part or an exact date. It fails, however, as a real-time discovery engine — a way to find content you may not have even known you wanted.

“We’re going to be on a collision course with Google,” Griffiths says.

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