Searchable podcast and audio libraries

A podcast back catalogue is a black box. There might be hundreds of hours of conversation in there, full of specific claims, recommendations, and moments worth finding again, and none of it is searchable. The only way to find where something was said is to remember which episode and roughly when, then scrub through the audio hoping you land near it. Everything that was said is in there; none of it is findable.

This page is for developers building something that fixes that: a podcast search feature, an audio app, a workspace over a media library. Extract transcribes the episodes automatically, Deep Search makes every word findable by meaning, and timestamps let a user jump straight to the moment. The back catalogue stops being an archive and becomes something you can query.

The problem

Audio is the least searchable format there is, because there's nothing to search until it's been transcribed. A library of episodes is, to any search system, just a set of opaque files. Before a single word can be found, every episode has to be turned into accurate text, and ideally text that knows when each part was said, so a result can point to the moment rather than just the episode.

Transcription alone doesn't get you there, though. A pile of raw transcripts is more searchable than audio but still frustrating, because keyword search over a transcript fails the way it always does: people in conversation rarely use the exact words a searcher would type. Someone looking for the episode about "investing for beginners" won't match a host who said "where to put your first thousand pounds," even though that's exactly the bit they want. Spoken language is loose and associative, which is precisely where keyword matching is weakest.

And then there's the jump. Finding the right episode is only half useful if the listener still has to hunt through an hour of it. The result needs to carry the timestamp, so "this is the episode" becomes "this is the minute." Producing that, accurate transcription, search that works on meaning, and timestamps that survive into the results, is the build standing between a back catalogue and a searchable one.

What Exabase unlocks

With transcription, semantic search, and timestamps underneath, an audio library becomes as searchable as a text one.

Episodes transcribe themselves. New audio runs through extraction and comes back as clean, timestamped text, so the library becomes text without anyone transcribing by hand, and the back catalogue can be processed in bulk to make the whole archive searchable at once.

Every word becomes findable by meaning. A search for a topic surfaces the moments where it was discussed, regardless of the exact words spoken, so the listener looking for "investing for beginners" finds the "first thousand pounds" segment. The library answers questions about its content rather than only matching literal phrases.

And the result is a moment, not just an episode. Because the transcript chunks carry timestamps, a search result points to the exact place in the episode, so the listener jumps straight there instead of scrubbing. That's the difference between knowing an answer is somewhere in your catalogue and being taken to it.

How it works

Three primitives carry this, with Extract turning audio into searchable text and Deep Search making it findable.

Extract

Extract is what turns audio into something searchable. You submit an episode and get back a clean transcript chunked with timestamps, so each searchable piece knows where in the episode it came from. It handles the long files podcasts produce, and the production concerns are built in: jobs retry on failure, and webhooks tell you when a transcription finishes, which matters when you're processing a back catalogue of hundreds of hours. The general bulk-processing case is covered in document extraction at scale.

Deep Search

Deep Search is what makes every word findable. It searches the transcripts at the paragraph level, semantically rather than by keyword, so a query matches what was meant rather than only what was literally said. It's hybrid by default, so a specific name or term still matches exactly while a loose topical query still works, and it holds quality as the library grows, avoiding the semantic collapse that degrades naive search at scale. Because the chunks carry timestamps, every result points to its moment in the audio.

Resources

Resources are where the transcripts live and get indexed. Each episode's transcript becomes a Resource, searchable through Deep Search, so the library is a store of searchable content rather than loose transcript files you manage. Storing them as Resources is what connects transcription to search as one flow.

Example architecture

The pipeline is transcribe, store, search, jump.

Process the catalogue. Run each episode through Extract to get a timestamped transcript, using webhooks to know when each finishes rather than polling. For a large back catalogue, submit in parallel.

Store as searchable Resources. Write each transcript to Resources, where it's indexed for Deep Search.

Search and jump. A user query runs a Deep Search across the library and gets back matching moments with their timestamps, so your app can link straight to the right point in the episode.

Keep it current. As new episodes publish, process them the same way, or use a Worker to handle new arrivals automatically.

Episodes flow in through transcription, become timestamped searchable Resources, and a query returns the exact moment. The audio archive becomes a searchable library.

What compounds over time

An audio search product gets more valuable as the library grows, which is the opposite of how an un-searchable archive ages.

Every episode added enlarges the searchable corpus, and because each is transcribed once, the cost of making it findable is paid a single time and the value lasts. A back catalogue that would otherwise get less navigable as it grows, more hours, more to hunt through, instead gets more useful, because more content means more questions it can answer. And because Deep Search holds quality at scale, a library of thousands of episodes stays as searchable as one of fifty.

Building this yourself means running a transcription pipeline, maintaining a search index that handles timestamps, and keeping retrieval quality up as the library grows, infrastructure that gets heavier as the catalogue does. Treating it as a managed flow means the archive becomes a growing, searchable asset while the work of running it stays flat.

Who's building this

Developers building podcast apps, audio platforms, media archives, and search features over spoken-word libraries, anywhere there's a catalogue of audio whose value is locked up because nobody can search it.

The closest neighbours are video content search, which applies the same transcription-and-search pattern to video, and media asset management for AI for mixed libraries. Meeting assistants use the same audio-and-timestamp capability for recorded meetings, and document extraction at scale covers the bulk-processing side.

Get started

Start with the getting started guide, then about extraction, submitting jobs, and extraction webhooks for the transcription side, and searching resources for the search side. There's a free tier to build against.

FAQs

How are episodes transcribed?

You submit audio to Extract and get back a clean transcript chunked with timestamps. It handles long files, and webhooks tell you when each transcription completes, so you can process a whole back catalogue without polling for status.

How does search find a topic when the host phrased it differently?

Deep Search matches by meaning rather than keyword, so a query for a topic surfaces the moment it was discussed even when the spoken words differ from the search terms. It's also hybrid, so an exact name or term still matches precisely.

Can users jump to the exact moment, not just the episode?

Yes. Transcript chunks carry timestamps, so a search result points to the place in the episode where the match occurs, and your app can link straight to that moment rather than the start of the file.

Will search hold up across a large back catalogue?

Yes. Deep Search is built to hold retrieval quality at scale, where naive search tends to suffer semantic collapse, so a library of thousands of episodes stays searchable.

How do I handle new episodes as they publish?

Process them through Extract the same way as the back catalogue, or set up a Worker to transcribe and index new arrivals automatically, as in the self-maintaining knowledge bases use case.

Does this work for video too, or only audio?

The same pattern applies to video. Extract handles both, and video content search covers the video case specifically.