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Particle's Radar platform transforms unstructured audio data into a structured knowledge layer, addressing a critical gap where AI agents currently lack access to vast repositories of spoken content. By indexing over 130,000 podcasts and providing an API for semantic search, the tool enables agents to retrieve specific quotes and insights from conversations that were previously inaccessible to text-only crawlers. This shift represents a significant expansion of the data frontier for AI systems, moving beyond static text to include dynamic audio intelligence.
Radar makes podcasts searchable — and usable by AI agents
Particle’s new podcast intelligence platform transcribes and analyzes more than 130,000 podcasts, making their conversations searchable on the web and accessible to AI agents through an API and MCP.
Particle, 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…
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