Transcribe a podcast to text you can actually search

Podcasts are one of the richest sources of long-form thinking on the internet, and one of the hardest to reuse. Audio is linear: to find the one insight you remember from an hour-long interview you have to scrub back and forth, and there is no way to grep through a season of episodes the way you would search a document. Transcribing a podcast to text fixes that. Once an episode is text, every sentence becomes searchable, quotable, and linkable back to the moment it was said.

Keldura is built for exactly this kind of media-heavy research. Paste a YouTube link for a video podcast, or upload an audio file for an audio-only show, and the episode is transcribed into clean, timestamped text and filed into a folder. If the show is published as a YouTube playlist or channel, you can import the entire back catalog in a single job rather than adding episodes one at a time — and an imported channel can auto-sync, adding new episodes on your plan's sync schedule, so your archive stays current without manual work. Channel and playlist import starts on the Starter plan.

The transcript is only the starting point. Because every episode lands in a searchable folder, you can ask questions across the whole show and get answers that cite the source episode and timestamp — so a claim is never floating without provenance. You can generate summaries automatically and keep podcast transcripts side by side with the videos, PDFs, and websites that make up the rest of your research.

This is the difference between a one-off transcription tool and a research workspace. Journalists building a quote database, analysts tracking what experts said across episodes, students turning interview shows into study notes, and creators repurposing long episodes into articles and clips all need the same thing: searchable, cited text they can come back to. Keldura keeps that text organized in persistent folders so the work compounds over time instead of living in scattered files.