Podcast transcripts AI — cite the exact timestamp

If you run a podcast, your back catalogue is your most underused asset. Two years of episodes = hundreds of hours of content that listeners can't search. They forget which episode discussed what; they don't come back to old episodes; you don't get the long-tail listens you should.

A SeekFiles AI assistant scoped to your transcripts fixes this — both for your audience (as a public Q&A bot) and for you (writing show notes, finding callbacks, repurposing content).

The setup

1. Transcribe every episode (Otter, Descript, Whisper — whatever you use).
2. Upload each transcript as S{n}E{m} - Episode Title.txt or similar.
3. Build a "Show Archive" Assistant scoped to all transcripts.
4. (Optional) Make it a public Assistant via slug so listeners can query.

Real questions for your own work

• "In which episode did we interview a doctor about sleep?"
• "Find every episode where we mentioned the company Acme — list episode + timestamp."
• "Pull the best 90-second quote from the past 10 episodes about the topic of resilience — I'm cutting a highlight reel."
• "What's been our most-recurring guest theme this year?"

Each answer cites the episode + the transcript paragraph (and often the timestamp if Otter or Descript captured it).

Real questions for your listeners (public bot)

If you make the Assistant public:

• "Did the show ever cover sleep?"
• "Which episode had the conversation about AI in education?"
• "What does the host think about [topic]?"

The listener gets the answer + episode number + (timestamp where available). They can jump right to the relevant point in their podcast app.

Why this beats Spotify / Apple search

Both platforms search episode titles and descriptions only. SeekFiles searches the transcript. A throwaway comment in episode 47 about kombucha gets found; an Apple Podcasts search doesn't.

Repurposing workflow

The single most valuable use: content repurposing.

• Newsletter / blog from podcast. Ask the Assistant for "best quote about X from past episodes" — pull together a thematic piece.
• Year-in-review. "What were the funniest exchanges this year?" or "Which guests had the best takeaways?"
• Onboarding new listeners. "If someone has never listened to the show, which 5 episodes should they start with?" — based on the actual content, not your guess.

Hygiene

• Guest consent. Make sure your guests are OK with their interviews being indexed and searchable. Most are fine but ask.
• Edit transcripts before public. Off-mic asides, false starts, things you'd never want quoted — clean before making public.
• Update show notes. Once you have an AI archive, your back-catalogue show notes can be much richer. AI helps draft them.

A note on monetisation

Podcast hosts in our user base report that:

• Public Q&A bots drive returning listeners to specific old episodes.
• "Top 5 quotes about X" blog posts (drafted from the assistant) drive new listeners from search traffic.
• Sponsor pitch decks reference specific past content ("our episode on Y, where Z said this") — easier to compile.

The ROI math: 30 minutes of setup per 100 episodes; payoff in audience engagement is months long.

Workflow

• Per-episode: transcribe → upload → re-index.
• Monthly: ask the Assistant for trend questions ("what topics did we cover most this month?").
• Quarterly: repurpose into newsletter or blog content.
• Annual: year-in-review post pulled from the Assistant's synthesis.

Most podcasts treat their back catalogue as past. The good ones treat it as inventory.