Depending on the file type, the content is split into logical parts in order to independently retain each's context
Each chunk is converted into a vector embedding, a numerical representation of its' meaning
You can then run semantic search queries such as the ones above to get relevant results.
The more you use it, the smarter it gets. Each interaction fine-tunes your specific search model.
Who: A student studying for an exam
What: Find specific content within a series of lengthy lecture videos
Why: Searching "mitochondria functions" for relevant discussions within a biology course.
Who: YouTube content creators or video editors
What: Locate specific clips within their video archives
Why: search for "laughing moments" or "funny fails" when compiling a highlight reel.
Who: Medical professionals
What: find specific content within educational or research videos
Why: a surgeon could search for "laparoscopic appendectomy procedure" within a database of surgical procedure videos
Who: Film and TV producers
What: find specific scenes or lines within a massive database of raw footage or scripts
Why: a director might search for "scenes in the rain" when compiling a mood board or reference reel
Who: Any business with an online presence
What: for media monitoring, analyzing how often their brand or products are mentioned in videos
Why: a search for "Brand X reviews" to find relevant content within a vast array of product review videos.
Who: Lawyers and paralegals
What: find specific statements or discussions within hours of deposition or courtroom footage
Why: a search for "defendant's testimony about the incident" to quickly locate relevant segments.