Radar by Particle

The Podcast Search Engine

Developer ToolsArtificial IntelligenceTech

▲ 0 votesLaunched Aug 31, 2026

Daily #5Weekly #9

The web is searchable. Podcasts should be too. Today, Particle is introducing Radar, the podcast search engine. Podcasts hold some of the most thoughtful and timely conversations happening right now, but that knowledge is hard to find. Radar searches 130,000+ actively transcribed podcasts, with ~20,000 new episodes daily. Millions of hours and billions of lines, fully searchable. Radar is powered by Particle’s Podcast Intelligence API, which allows agents to search across podcasts via API/MCP.

AI Analysis

📝 Summary

Radar by Particle is a podcast search engine indexing 130,000+ transcribed podcasts, adding ~20,000 episodes daily for billions of searchable lines. It solves the key pain point that insightful, timely podcast conversations are hard to discover unlike web content. Core features include full-text search and the Podcast Intelligence API for AI agents via API/MCP. USP is transforming podcasts into an accessible, machine-readable knowledge base. Value proposition: Enables quick access to thoughtful discussions for research, content creation, and AI applications.

📈 Market Timing

In 2025-2026, timing is excellent due to maturing AI transcription, semantic search, and agent technologies (e.g., MCP). Podcast consumption is growing for in-depth content while user demand shifts toward precise, on-demand knowledge extraction. AI tool adoption is accelerating amid favorable tech investment environment. Excellent Timing.

✅ Feasibility

High feasibility. Technical foundation exists via Particle's API and transcription pipeline, though scaling real-time indexing of 20k daily episodes requires significant compute. Development/operation costs are high for AI processing and storage but manageable with cloud. Low regulatory risks; strong scalability potential. Rating: High.

🎯 Target Market

Main segments: AI/ML developers building agents (tech industry), researchers, journalists, and knowledge workers seeking timely insights; primarily English-speaking regions (US, Europe). TAM for podcast tech/AI search ~$1B+, SAM for specialized search tools ~$200M, SOM for API users ~$50M. Core pain: time wasted scanning long episodes. High willingness to pay for API/pro features.

⚔️ Competition

Medium. Direct competitors: 1. Listen Notes (listennotes.com), 2. Podchaser (podchaser.com), 3. Podcast Index (podcastindex.org), 4. Spotify Podcast Search. Advantages: Massive daily-updated transcribed index, specialized AI agent API/MCP integration, focus on deep search. Disadvantages: Newer entrant vs. established players with larger brand recognition and broader features; potential data overlap.

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