Every video editor knows the feeling. You've got the vision. You can see the final piece in your head. But between you and the creative work stands a wall: hundreds of hours of documentary footage that needs to be searched, scrubbed, and sorted before you can touch a single cut.
It's not the editing that burns people out of this industry. It's the hunting. The endless searching through interview footage for those few perfect moments buried somewhere in hour two. The manual logging. The tedious rewatching of content you've already seen three times.
What if you could search your video footage the way you search the internet?
Imagine describing what you're looking for in plain language—the way you'd explain it to an assistant editor—and having an AI find it for you. Not an approximation. The actual moment, pulled from your footage, ready for your timeline.
This is what natural language video search makes possible. Instead of scrubbing through a three-hour interview to find the thirty seconds where your subject talks about their childhood, you just ask for it. The AI handles the transcript analysis, the metadata correlation, the frame-by-frame hunting. You get a collection of relevant clips in minutes.
This is what I'm building with Vindexr. It uses large language models to understand your footage through transcripts, visual analysis, and metadata, then lets you query it conversationally.
The tedious work gets delegated to the machine. The creative work stays with you.
If you've ever lost an afternoon to footage logging when you should have been editing, this is the workflow upgrade you've been waiting for.