Particle, the startup founded by former Twitter engineers, is betting that the real money in AI isn’t in summarizing text—it’s in making spoken audio searchable. The company just launched Radar, a podcast search engine that transcribes over 130,000 shows, adds speaker labels and entity recognition, and lets users pull out timestamped quotes without listening to a full episode. That’s a staggering volume: 20,000 new episodes indexed every day, covering all Apple Top 200 podcasts across 135 categories.
Hedge funds, it turns out, are the earliest adopters. CEO Sara Beykpour says they’re the highest-volume customers integrating directly with the API. That makes sense when you think about what’s at stake—podcast conversations are where executives, analysts, and insiders often say things they’d never put in an SEC filing or a press release. AI agents that only crawl text on the web are completely blind to that signal. ‘Agents are generally blind to audio,’ Beykpour told TechCrunch. ‘They can’t see it unless something or someone has transcribed it.’
The product goes beyond simple transcription. Radar tracks mentions of people, companies, brands, and products across episodes, then sends alerts via email, Slack, or webhook when those entities come up. Users can filter alerts to fire only when specific guests discuss specific topics. There’s even a dedicated ads search engine that finds every episode where a given company advertises and tracks how that activity trends over time. Beykpour frames the curated clips as a middle ground: ‘We’ve pre-chosen notable clips, so if you can’t listen to the whole podcast and you don’t want to read a summary, this is the best way to just get an idea of what’s happening in that podcast.’
Particle’s pivot happened because the podcast intelligence was already working inside its news-reading app—it was just trapped there. The team realized the underlying API had broader value as the AI agent ecosystem exploded. Radar is priced at $29 per month per seat, with a $399 business tier covering 20 seats; API customers get custom pricing. The company plans to expand beyond podcasts into YouTube videos and news clips. That’s the logical endgame: any spoken audio becomes a queryable dataset. The question is whether Particle can outrun the big platforms—Google, Spotify, Apple—who already have the audio and the infrastructure to build the same thing if they decide it’s worth the effort.
