Workflows
What Is AI Video Search and How Does It Work
AI video search indexes the content of your footage. How it differs from filename search, the four modalities behind it, and why the index lives on your machine.
You have 10 TB of footage across six external drives and need a wide shot of a warehouse with forklifts from two months ago. Finder and Explorer see filenames, dates and sizes. They do not see what is in the video.
AI video search analyses the footage and builds an index from its content. You type "warehouse with forklifts" and get timestamped results across the library.
A001_C012_0814KN.R3D
personLena detected at 04:10, 21:44, 38:02
C0034_sunset_harbor.MP4
sceneGolden hour establishing shot, harbor with boats
DOC_Interview_EP02.mp4
transcript"...quarterly goals and marketing strategy across all channels..."
Filename search
Finding footage by filename, folder, manual tag or a spreadsheet kept by a production coordinator depends on someone having written the right words down. If nobody tagged a clip as "warehouse" and the file is A021_C003_0214K7.R3D, no search connects it to the query.
A good naming convention gets you to the right folder or camera card. It does not get you to the right moment in a 45-minute clip.
AI video search
AI video search runs models over the video and audio, extracts structured information and stores it in a search index. A query searches what happened in the video. The analysis usually covers four modalities.
Transcription
Speech-to-text turns everything said on camera into timestamped text: interviews, dialogue, voiceover, ambient conversation. Speaker diarization labels who said what. A search for "we need to revisit the timeline" returns the moment it was said.
Transcription is the most mature of the four and works well on clear audio.
Object detection
Vision models label the objects in each frame: vehicles, laptops, coffee cups, tools, animals, signage, food. Nobody has to describe the footage by hand.
Object detection matters most for B-roll, product shots and any footage where what is on screen matters more than what is said.
Scene descriptions
A model writes a summary of each shot. "Two people shaking hands in a lobby." "Aerial shot of a construction site." "Close-up of a circuit board being soldered."
Descriptions carry context that object labels do not. "Laptop" is one thing. "Someone presenting to a group while pointing at a laptop" is another.
Face recognition
Faces are detected, clustered and optionally named across the library. A search for a person returns every clip they appear in, from any angle, lighting or shoot.
This pays off on multi-day shoots, multicam productions and any project that tracks one person across many clips.
The index
The four passes produce timestamped transcripts, object labels, scene descriptions and face clusters. FrameQuery stores them in a local index built on Tantivy, a Rust search engine. The index lives on your machine and search works offline.
The index is small next to the footage. Hours of video produce megabytes of index.
A query checks all four modalities at once. "Sarah explaining the prototype" can match Sarah's face, the word "prototype" in the transcript and a prototype on screen. Results are ranked and shown with thumbnails.
Why local
Most AI video search tools require uploading the footage to a cloud service. For a production with terabytes of cinema RAW that is impractical, and for sensitive content it may be off the table.
FrameQuery extracts frames and audio on your device and sends only those for cloud processing. Originals stay on your machine and the extracted data is discarded when analysis completes. Face and voice recognition run on your device, so biometric data stays local. The index is a local file.
There is no per-query cost, no internet requirement after indexing and no footage on someone else's servers.
What it does not do
It does not decide whether a clip works in the edit.
It does not reach 100% recall. Ambiguous audio, unusual objects and fast-moving scenes leave gaps.
It does not organise your projects, bins or timelines.
Where it sits
AI video search sits between ingest and editing. Shoot, point the tool at the media, process it, then search whenever you need something. The index persists, so footage processed months ago is still searchable.
Download FrameQuery to try AI video search.