AI infrastructure for media

Media organisations sit on archives that are simultaneously their most valuable asset and their least usable one. Decades of audio and video, interviews, broadcasts, footage, recordings, all full of material worth finding again and almost none of it searchable. The only way into a back catalogue is usually to know which recording and roughly when, then scrub. If you're building content tools for media, the infrastructure you need underneath is transcription that scales to a huge archive, search that finds by meaning with timestamps, and a way to keep the library current as new content arrives.

This page is for teams building media and content tools. Exabase gives you transcription through Extract, meaning-based search with timestamps through Deep Search, and autonomous upkeep through Workers. Together they turn a back catalogue from an archive nobody can search into a queryable knowledge base.

What you can build

Media content tools tend to be one of a few shapes, each on infrastructure that already exists.

A searchable audio archive that transcribes a back catalogue and makes every word findable by meaning, with timestamps so a listener jumps to the moment, the searchable podcast and audio libraries pattern.

A video search platform that does the same for video, broadcasts, interviews, footage, so a question returns the exact moment rather than a recording to rewatch, video content search.

A mixed-media asset manager that processes audio, video, images, and documents through one pipeline and keeps the library organised as it grows, media asset management for AI.

A content-intelligence tool that lets editors and producers query an entire archive, finding everything said on a topic across years of material, Deep Search over a transcribed library.

Media problems, solved

The problems media builders run into are specific, and each has an answer.

Archives locked in audio and video. There's nothing to search until it's transcribed. Extract transcribes audio and video with timestamps through one API and handles the scale a decades-long archive involves, with retries and webhooks for processing a back catalogue without babysitting.

Finding the moment, not just the recording. A result that points to the right broadcast but leaves an hour to scrub isn't much use. Because the transcript chunks carry timestamps, Deep Search results point to the exact moment, so a query lands on the minute rather than the file.

Search that matches meaning. People speaking on air phrase things the way people speak, not the way someone later searches. Deep Search matches by meaning, so a topic search surfaces the relevant moment regardless of the exact words, and holds quality across a vast archive where naive search collapses.

Keeping a growing library current. New content arrives constantly. Workers transcribe and index new material on a schedule, so the searchable library stays current without manual processing, the self-maintaining knowledge bases pattern applied to a media archive.

Mixed formats in one place. Archives are audio, video, images, and documents together. Media asset management processes all of it through one pipeline into a single searchable library.

The infrastructure underneath

Four primitives carry most media tools. Extract transcribes audio and video and processes other media at scale. Deep Search makes every word findable by meaning, with timestamps. Workers keep the library current as new content arrives. Resources store the transcribed, searchable content. Bases isolate per organisation or per client if you're building this as a product for multiple media companies. One API key, rather than a transcription pipeline, a timestamp-aware index, and a job runner you build and maintain.

Back catalogues that become more valuable as they grow

A media content tool on this foundation inverts how an archive ages. Normally a back catalogue gets less navigable as it grows, more hours, more to scrub. On this foundation every recording transcribed becomes part of a searchable library, paid for once and findable forever, so a larger archive means more questions it can answer, not more to hunt through. Because Deep Search holds quality at scale, a library of thousands of hours stays as searchable as a handful, and because Workers keep processing new content, the library grows and stays current at the same time. A decades-long archive turns from a cost into a queryable asset, while the work of running it stays flat.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: searchable podcast and audio libraries, video content search, and media asset management for AI for mixed libraries. There's a free tier to build against.

FAQs

Can it transcribe a large back catalogue of audio and video?

Yes. Extract transcribes audio and video with timestamps through one API and handles the scale of a decades-long archive, with webhooks signalling completion so a back catalogue can be processed in bulk without polling.

Do search results point to the moment or just the recording?

To the moment. Transcript chunks carry timestamps, so a Deep Search result identifies the exact point in the exact recording, and your tool can link straight there rather than to the start of the file.

Does search find a topic when it was phrased differently on air?

Yes. Deep Search matches by meaning, so a topic search surfaces the relevant moment even when the spoken words differ from the query. It's hybrid, so a specific name still matches exactly.

How does the library stay current as new content arrives?

Workers transcribe and index new content on a schedule, so the searchable library stays current without manual processing. It's the self-maintaining knowledge bases pattern applied to a media archive.

Can it handle mixed media, not just one format?

Yes. Media asset management for AI processes audio, video, images, and documents through one pipeline into a single searchable library, kept organised by Workers.

Is this a finished media product or something I build on?

Something you build on. Exabase is the infrastructure, transcription, timestamped search, and autonomous upkeep, and you build the archive, search platform, or content tool on top.