Media asset management for AI

Studios, agencies, and creators sit on large libraries of mixed media: audio, video, images, documents, all jumbled together and all hard to find anything in. Traditional asset management handles the storing and the foldering, but it doesn't understand what's in the files, so finding the clip where someone said a particular line, or the image that matches a brief, still comes down to someone remembering where it is. The library grows; the findability doesn't.

This page is for developers building AI-native asset management: a media platform, a digital asset manager, an internal library for a studio or brand. Exabase processes every media type through one API, stores it as searchable Resources, and uses Workers to keep the library organised and current, so you get content-aware asset management without building a separate pipeline for each format.

The problem

A mixed-media library is several extraction problems at once. Audio and video need transcribing, documents need their text pulled out, images need processing, and each format wants a different tool. Building asset management that actually understands its contents means assembling and maintaining a pipeline per media type, which is a lot of infrastructure before you've built any of the features a user touches.

Then the contents have to be searchable in a way that crosses formats. A studio doesn't want to search audio in one place and video in another; they want to ask a question and get back the relevant assets whatever type they are. That needs a single searchable index over everything once it's processed, and search that works on meaning rather than keywords, because the words in a brief rarely match the literal contents of the asset that answers it. Search that degrades as the library grows is its own trap, the semantic collapse problem, and media libraries get very large.

And a library is never static. New assets arrive constantly, and keeping everything processed, organised, and current is ongoing work. A library that's only as organised as the last time someone tidied it manually drifts out of order the moment people stop tending it, so the upkeep is either a standing chore or it doesn't happen and the library rots. Building all of this, multi-format processing, unified search, and continuous organisation, is the pipeline standing between raw assets and an asset manager that earns its name.

What Exabase unlocks

With one processing pipeline, unified search, and autonomous upkeep, a mixed-media library becomes content-aware and stays that way.

Every format goes through one pipeline. Audio, video, images, and documents are all processed through a single API and land as searchable Resources, so you're not maintaining a separate ingestion path per media type. The library becomes uniformly searchable regardless of what kinds of files are in it.

Search crosses formats and works on meaning. A query returns the relevant assets whatever their type, matched on what they contain rather than their filenames or tags, so a brief surfaces the clip, the image, and the document that fit it together. Finding an asset stops depending on whoever filed it having tagged it the way you'd later search.

And the library keeps itself in order. Workers process new assets as they arrive, tag and organise them, and keep the searchable index current, so the library stays organised without a standing manual effort. It grows and stays navigable at the same time, which is the thing a manually tended library can't manage for long.

How it works

Four primitives combine here: Extract for the multi-format processing, Resources and Deep Search for the unified searchable library, and Workers for keeping it current.

Extract

Extract is the single pipeline for every media type. Audio and video are transcribed with timestamps, documents are turned into clean text, and it all comes back through one API regardless of format, so the per-format ingestion problem becomes one submission flow. It handles the large files media libraries are full of, with retries and webhooks for processing at volume. The general case is document extraction at scale, and the format-specific search patterns are covered in audio and video search.

Resources

Resources are the unified library. Every processed asset, whatever its original format, becomes a Resource, so audio, video, images, and documents live in one store and are searched together rather than in separate silos. Resources carry metadata and can be organised and tagged, which is what makes this asset management rather than just search.

Deep Search

Deep Search is the cross-format search over the library. It matches by meaning at the paragraph level, so a query finds the relevant content inside any asset type, and for audio and video the timestamped chunks mean a result points to the exact moment. It holds quality as the library scales, so a vast asset library stays searchable rather than degrading into semantic collapse.

Workers

Workers keep the library organised and current. A Worker runs on a schedule and processes new assets as they arrive, tags and organises them, and keeps the index up to date, all without manual triggering. This is what turns a one-time import into a living library that stays in order as it grows, the same autonomous-upkeep engine behind self-maintaining knowledge bases.

Example architecture

The structure is one pipeline in, one searchable library, kept current by Workers.

Process everything through one path. Send assets of any type to Extract and store the results as Resources, indexed for Deep Search. Audio and video come back timestamped; documents come back as text.

Search across the whole library. A query runs a Deep Search over every asset regardless of type, returning the relevant ones with, for time-based media, the exact moment.

Let Workers keep it current. Set up a Worker to process new arrivals, tag and organise them, and maintain the index on a schedule, so the library stays organised on its own.

Isolate by client or project if needed. For an asset-management product serving multiple clients, give each their own Base for full isolation.

Mixed media flows in through one pipeline, becomes one searchable library, and Workers keep it organised as it grows. The asset-management pipeline you'd otherwise build per format is the platform.

What compounds over time

An AI-native asset manager gets more valuable as the library grows, where a traditional one gets less navigable.

Every asset added becomes part of a searchable, organised library, processed once and findable forever, and because Workers keep organising new arrivals, the library stays in order as it scales rather than drifting the moment manual tidying stops. Because Deep Search holds quality at scale, a library of hundreds of thousands of assets stays as searchable as a small one, and because the underlying organisation self-maintains, growth doesn't translate into disorder.

Building this yourself means a processing pipeline per format, a unified index to maintain, and a standing effort to keep the library organised, infrastructure that gets heavier exactly as the library becomes more valuable. As a platform, the library becomes a growing, self-organising asset while the operational work stays flat, which is what makes content-aware management of a large media library feasible rather than a permanent backlog.

Who's building this

Studios, creative agencies, media companies, and platforms building asset management for large mixed-media libraries, anywhere audio, video, images, and documents pile up and finding the right one depends on more than filenames and folders. Developers building a DAM product, an internal media library, or a content platform are the fit.

The format-specific neighbours are searchable podcast and audio libraries and video content search; this page is the mixed-media superset of both. Self-maintaining knowledge bases covers the autonomous-upkeep engine, document extraction at scale the processing side, and multi-tenant memory for SaaS the per-client isolation if you're building this as a product.

Get started

Start with the getting started guide, then about extraction and submitting jobs for processing, searching resources for the unified search, and about Workers for keeping the library current. There's a free tier to build against.

FAQs

Can it really handle audio, video, images, and documents through one API?

Yes. Extract processes every supported media type through a single submission flow, transcribing audio and video with timestamps and turning documents into text, so you don't maintain a separate pipeline per format.

Can I search across all media types at once?

Yes. Every processed asset becomes a Resource in one library, and Deep Search searches across all of them by meaning, returning the relevant assets whatever their type. For audio and video, results carry the exact timestamp.

How does the library stay organised as new assets arrive?

A Worker processes new arrivals, tags and organises them, and keeps the index current on a schedule, so the library stays in order without manual upkeep. This is the same engine behind self-maintaining knowledge bases.

Will search hold up across a very large library?

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

Can I run isolated libraries for different clients?

Yes. Give each client their own Base for full isolation, following the multi-tenant SaaS pattern, if you're building asset management as a product for multiple customers.

How is this different from the audio and video search use cases?

Those cover a single format each; this is the mixed-media superset, one pipeline and one searchable library across audio, video, images, and documents together, with Workers keeping the whole thing organised. If you only have audio or only video, the audio or video page is the more focused fit.