Particle, a startup initially focused on AI newsreading, has shifted its core business to podcast indexing with the introduction of Radar. The company, founded by former Twitter engineers, recognized a market need for making spoken audio content discoverable and usable by AI systems.
Radar builds upon a feature from Particle's news-reading app that sourced podcast clips. The company decided to develop this capability into a standalone API product as the demand for AI agents grew.
Radar functions as a podcast search engine that not only transcribes audio but also interprets its meaning. It identifies entities such as people, companies, brands, products, and topics discussed within podcasts, providing rich metadata and speaker labels.
The service currently transcribes over 130,000 podcasts, including all Apple Top 200 podcasts across 135 verticals, with 20,000 new episodes indexed daily. This makes it the largest transcribed podcast service available.
Radar's primary business model involves providing an API for AI agents and data resellers. Hedge funds have emerged as the highest-volume customers, integrating directly with the API to access data not typically available to their AI agents, which primarily crawl text-based web content.
Other users include journalists, researchers, and AI search platforms like Exa, which is a partner. Radar also offers customizable alert systems for tracking entity mentions across podcasts, delivered via email, Slack, or webhook.
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Particle, an AI newsreader startup, has launched Radar, a podcast search engine that transcribes and semantically understands podcast audio. Radar provides an API for AI agents and data resellers, with hedge funds being early adopters for accessing unstructured audio data.