Particle, an AI newsreader startup founded by former Twitter engineers, has pivoted to focus on indexing and making discoverable the spoken conversations within podcasts through its new product, Radar. Radar is a podcast search engine that not only transcribes podcast audio but also comprehends the content, enabling it to extract key quotes and highlights. The company claims that Radar transcribes over 130,000 podcasts, including all Apple Top 200 podcasts across 135 verticals, with 20,000 episodes added to its index daily. Read more
Radar’s transcriptions include speaker labels and rich metadata, allowing the system to understand and track entities such as people, companies, brands, products, and topics discussed in the podcasts. The platform can send customizable alerts via email, Slack, or webhook when specific entities are mentioned, and it can extract self-contained clips with timestamps for users to listen to or read. Additionally, Radar offers features like tracking podcast topics, listener ratings, reviews, and advertising data, which present monetization opportunities. The core product is an API and MCP (Media Content Platform) that enables AI agents and businesses to programmatically access this podcast intelligence. Radar is priced at $29 per month per seat, with a $399-per-month business plan including 20 seats, while API users have custom pricing based on their needs. The company plans to expand its service beyond podcasts to support other audio formats, such as YouTube videos and news clips. Read more