Particle, an AI newsreader startup founded by former Twitter engineers, has launched Radar, a podcast search engine that transcribes and analyzes over 130,000 podcasts, making their audio content searchable and accessible. Unlike traditional search platforms that focus on text, Radar dives deep into podcast conversations, extracting key quotes, highlights, and metadata such as speaker labels and entities like people, companies, and topics. This innovation allows Radar to serve industries reliant on uncovering hidden audio insights.

The company revealed that hedge funds are among Radar’s most active customers, leveraging its API to access data that often goes unnoticed in spoken content. Other key users include AI search platforms and data resellers, such as Exa, which integrates with Radar to enhance AI agents’ capacity to comprehend audio information. Radar’s framework allows for tailored alerts on specific guests, topics, or podcasts, delivered via email, Slack, or webhook, enabling timely notifications about relevant mentions.

Radar’s podcast intelligence goes beyond transcription by offering features such as notable clip extraction with timestamps, podcast ad tracking, political bias analysis, audience size estimates, and brand suitability monitoring. These functionalities expand the potential for monetization and usage across media intelligence and marketing sectors. Although the platform is accessible through a web interface, its core offering is the API and managed cloud platform (MCP) that businesses and AI agents use for programmatic access to its audio intelligence.

Currently, Radar’s pricing starts at $29 per month per seat, with an option for businesses to pay $399 monthly for 20 seats, while custom pricing applies for API users. Looking ahead, Particle plans to extend Radar’s capabilities beyond podcasts to include other audio formats such as YouTube videos and news clips, further broadening the scope of audio content analysis in the AI space. This pivot builds on Particle’s earlier AI features and represents a strategic move to capitalize on the growing demand for audio data discovery.