Scrape podcasts
till text you can search.

A different approach to scraping podcasts.

Podcast scraping is the practice of pulling data out of episodes: the audio, the show notes, the RSS feed, sometimes the transcript, so a team can study what shows and guests are actually saying. Market researchers, SEO teams, and PR desks have used it for years to spot trends, track competitors, and find quotes worth citing.

Why manual podcast scraping breaks down

For most teams the process never matched the promise. Someone downloaded an RSS feed, queued a handful of episodes, and pressed play at 1.5x speed hoping to catch the one line about a competitor’s pricing. Scraper tools pulled titles and show notes but never the actual dialogue. A ninety-minute episode became two hours of a researcher’s afternoon, and most of it still went unlistened.

Reading the episode, not just the feed

Speak AI treats every podcast episode the way a research analyst would, at machine speed. Each episode is transcribed in your language, with 100+ supported, and then the recording itself is read: the tone and energy of the guest, the emphasis in their delivery, the words they chose. Names, brands, topics, and claims are extracted into structured fields your systems can use, so a report is built from what was actually said, not from a scraper’s best guess at the show notes.

Then the questions start. Ask across your entire podcast library with AI chat, using the same prompt workflows research teams once stitched together manually, now running natively over your episodes with ChatGPT, Claude, and Gemini built in.

What teams ask their podcast library

• “Which episodes this quarter mention our product or a competitor by name?”
• “What do guests say about pricing, and how has that language changed since last year?”
• “Pull every quote where a guest talks about switching tools or vendors.”
• “Summarize the three most common objections guests raise about our category.”
• “Show me every episode where sentiment about us was negative.”

From a full feed to a research report

The result is a podcast library that answers instead of one that has to be replayed. Competitor mentions surface on their own. Quotes land in a brief instead of a half-remembered paraphrase. Trends across hundreds of episodes become a report instead of a hunch, and dashboards you can customize and white-label track topic frequency and sentiment over time, so this month’s episodes are measured against last quarter’s. Third Door Media put its conference video library through this workflow and turned 500 hours of conference video into high-performing content, without adding headcount.

And because podcasts rarely live alone, the same engine works across meetings, interviews, and calls, connecting your podcast library to MCP so any assistant in your stack can query it directly.