GoKawiilParticle, the AI newsreader startup founded by former Twitter engineers, is shifting its focus to a potentially more lucrative idea: indexing the spoken conversations buried in podcasts and making them discoverable. On Wednesday, the company introduced Radar, a podcast search engine that not only transcribes podcast audio but also understands what it means, enabling it to pull out key quotes and highlights.
The solution has business potential, as it’s already attracted interest from hedge funds looking for data that their agents can’t see, explains Particle co-founder and CEO Sara Beykpour.
“Hedge funds have been the highest-volume customers that are directly integrating with the API,” Beykpour told TechCrunch. While journalists and researchers could also make use of the tools, other top-paying customers have included AI search platforms and data resellers. (The search API provider for AI agents, Exa, for instance, is among Radar’s partners.)
Image Credits:Particle/Radar
The idea itself stemmed from one of the Particle news-reading app’s most beloved features. The app had used an API to source interesting podcast clips that it then included alongside related news stories in the app’s feed.
Particle’s team realized the product’s value, but also that it was somewhat trapped in the news reader. As the movement around AI agents began to gain steam, the company decided to pivot and focus on building an API for its podcast intelligence product.
Podcast Search Engine Image Credits:Particle/Radar
“Our vision is really to have all new media intelligence and all audio intelligence in that API. One of the reasons why it’s an interesting space is that most API agents and services crawl the web and they’re focused on text. We are providing that layer with audio,” Beykpour said. “Agents are generally blind to audio; they can’t see it unless something or someone has transcribed it.”
With Radar, the company transcribes more than 130,000 podcasts, making it the largest transcribed podcast service in existence. This includes all the Apple Top 200 podcasts across its 135 verticals, with 20,000 episodes added to Radar’s index daily.
The podcast transcriptions include speaker labels and rich metadata, as Radar understands the entities — people, companies, brands, products, and topics — being discussed.
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