Video content search and retrieval

Training videos, lectures, interviews, webinars, ads, vlogs, raw footage, organisations and creators sit on enormous libraries of video, and almost all of it is unsearchable. The information is in there, often the most useful information anyone recorded, but getting to a specific moment means knowing which video and roughly where, then scrubbing. Ask "what did the CEO say about pricing in the Q3 all-hands?" and the honest answer from most systems is "it's somewhere in that two-hour recording, good luck."

This page is for developers building search and retrieval over video: a video platform feature, an internal training search, a tool for editors trawling footage. Exabase transcribes the video, chunks it by timestamp, and makes every word searchable by meaning, so that question returns an answer pointing to the exact minute, rather than a suggestion to rewatch.

The problem

Video is doubly hard to search. Like audio, there's nothing to search until it's transcribed, so a library of recordings is opaque to any search system until every video has been turned into accurate, timed text. But video also tends to be longer and more abundant than audio, hours of lectures, full webinars, entire shoots, so the cost of not being able to search it is higher, and the scrubbing penalty for finding the wrong spot is worse.

Transcription gets you text, but text you search with keywords still disappoints, because people speaking on camera phrase things the way people speak, not the way someone later searches. A trainer explaining a process won't use the words a new hire types into the search box, even though the segment is exactly what they need. The match has to be on meaning, or the library stays effectively unsearchable even after it's transcribed.

And the whole point is to avoid rewatching, which means a result has to land on the moment. Finding the right video out of a thousand is a weak result if the user then faces an hour of it. For an editor combing footage for a specific line, or a viewer chasing one claim in a webinar, the timestamp is the deliverable. Producing all of it, accurate transcription of long video, search on meaning, and timestamps that point into the recording, is the build between a video archive and a searchable one.

What Exabase unlocks

With transcription, semantic search, and timestamps underneath, a video library becomes navigable by question rather than by memory.

Video transcribes itself. Recordings run through extraction and come back as clean, timestamped text, so a whole library, training catalogue, webinar archive, footage store, becomes searchable text without anyone transcribing by hand, and the back catalogue can be processed in bulk.

Every word becomes findable by meaning. A search surfaces the segments where something was discussed regardless of the exact words on camera, so the question about Q3 pricing finds the moment the CEO addressed it even if they never said the word "pricing." The library answers questions about its content instead of matching literal phrases.

And the answer is a timestamp. Because the transcript chunks are timed, a result points to the exact moment in the exact video, so a viewer jumps straight there and an editor lands on the frame they wanted, no rewatching. That's the difference between a library you have to remember your way around and one you can ask.

How it works

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

Extract

Extract turns video into searchable text. You submit a video and get back a transcript chunked with timestamps, so each searchable segment knows its position in the recording. It's built for the long files video produces, and the production concerns are handled: jobs retry on failure, webhooks signal completion, and thumbnails are generated, which matters when you're processing a library of long recordings. The general bulk case is covered in document extraction at scale.

Deep Search

Deep Search makes the transcribed library findable. It searches at the paragraph level by meaning rather than keyword, so a query matches the intent of what was said, and it's hybrid, so a specific product name or person still matches exactly while a conceptual query still works. It holds quality as the library grows, avoiding the semantic collapse naive search hits at scale. Every result carries the timestamp from its chunk, so search points into the video, not just at it.

Resources

Resources hold the transcripts and keep them indexed. Each video's transcript becomes a Resource searchable through Deep Search, so the library is a store of searchable content rather than a folder of transcript files, and transcription connects to search as one flow.

Example architecture

The pipeline is transcribe, store, search, jump, the same backbone whether the library is training videos or raw footage.

Process the library. Run each video through Extract for a timestamped transcript, using webhooks to know when long jobs finish. Submit in parallel for a large catalogue.

Store as searchable Resources. Write each transcript to Resources, indexed for Deep Search.

Search and jump. A query runs a Deep Search across the library and returns matching moments with timestamps, so your app links straight to the right point in the right video.

Keep it current. Process new uploads the same way, or use a Worker to transcribe and index new videos automatically.

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

What compounds over time

A video search product gets more valuable as the library grows, the reverse of how an un-searchable archive ages.

Every video added enlarges the searchable corpus, and since each is transcribed once, the cost of making it findable is paid a single time while the value persists. A library that would otherwise get harder to navigate as it grows, more hours, more to scrub, instead gets more useful, because more content means more questions it can answer with a precise moment. And because Deep Search holds quality at scale, a catalogue of thousands of long videos stays as searchable as a handful.

The build-it-yourself path gets heavier as the library does: a transcription pipeline to run, a timestamp-aware index to maintain, retrieval quality to defend as hours accumulate. As managed infrastructure, the archive becomes a growing, searchable asset while the operational work stays flat, which is what makes searching a large video library practical rather than perpetually almost-done.

Who's building this

Developers building video platforms, training and learning systems, webinar archives, media libraries, and footage-search tools for editors, anywhere there's video whose value is trapped because it can't be searched. The same capability serves both the viewer asking a question and the editor hunting a specific line in hours of footage.

The closest neighbour is searchable podcast and audio libraries, which applies the same pattern to audio, and media asset management for AI for mixed media libraries. Meeting assistants use the same transcribe-and-timestamp capability for recorded meetings, and document extraction at scale covers bulk processing.

Get started

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

FAQs

How is video transcribed and made searchable?

You submit video to Extract and get back a transcript chunked with timestamps, stored as Resources and indexed for Deep Search. Webhooks tell you when long transcriptions finish, so a large library can be processed without polling.

Will a search find something the speaker phrased differently?

Yes. Deep Search matches by meaning, so a query about a topic surfaces the segment even when the on-camera wording differs from the search. It's hybrid too, so a specific name or term still matches exactly.

Does a result point to the moment, or just the video?

To the moment. Transcript chunks carry timestamps, so a result identifies the exact point in the exact video, and your app can link straight there rather than to the start of the recording. This is what replaces "rewatch it" with "jump to it."

Is this useful for editors working with raw footage?

Yes. The same search-and-timestamp capability lets an editor find a specific line or moment across hours of footage by meaning, rather than scrubbing through clips, which is one of the main reasons to build search over a footage library.

Will it hold up across a large video catalogue?

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

Does this also handle audio?

The same pattern works for audio. Extract handles both, and searchable podcast and audio libraries covers the audio case specifically.