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AyeWatch detects meaningful changes across billions of web sources and only alerts you when it matters.
Podcasts contain substantial intelligence that never appears in written form — analyst interviews, executive conversations, expert discussions, and off-the-record commentary that gets published in audio form. For researchers, investors, and journalists, knowing when a topic you're tracking comes up in podcast coverage is genuinely valuable. The challenge is that podcast content is audio, not searchable text.
How Podcast Monitoring Works
The monitoring layer that exists for podcasts is the transcript and summary layer. Most high-production podcasts now generate transcripts (via Spotify, or third-party tools). Additionally, services like Podchaser, Listen Notes, and individual podcast sites publish episode summaries and descriptions that describe episode topics.
AyeWatch monitors this layer: podcast descriptions, episode summaries, transcript excerpts, and secondary coverage (articles and news about specific podcast episodes). A topic like "[Show name] episodes covering [topic area]" catches relevant episodes as they're indexed in the ecosystem.
The Better Use Case: Monitoring Across Many Shows
The real power isn't tracking one show — it's tracking a topic across many shows. "Podcast episodes discussing AI regulation and government policy toward large language models" covers the topic across hundreds of relevant podcasts simultaneously. When any show in the universe of AI/tech podcasts covers the topic, the monitoring catches it.
Monitor podcast topics across the entire ecosystem
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