Tracenode converts video into a dense timestamped text format — indexable, structured, and parseable by LLMs to reason over video content without requiring access to raw footage.
| TIME | VIDEO | AUDIO | BG | ON-SCREEN | NOTES |
|---|---|---|---|---|---|
| 00:12 | Speaker enters frame from left, wearing blue jacket, gesturing toward camera | "Welcome back to the channel. Today we're diving into..." | Office interior, desk, bookshelf | Channel logo top-right | Intro segment |
| 01:45 | Cut to screen recording, cursor moving across interface, clicking toolbar | "So if you click here, you'll see the settings menu appears..." | Desktop app UI | Menu: File, Edit, View, Settings | Demo starts |
| 03:22 | Return to speaker, close-up shot, nodding, making eye contact with camera | "That's the key difference between the two approaches." | Same office, slightly blurred | — | Transition point |
Real structured output from a long-form video
Three steps to structured video text
Drop your video file. MP4, MOV, AVI, MKV supported. No length limits — built for long-form content.
Each frame is analyzed for visual changes, audio is transcribed at the word level, and on-screen text is captured — grounded in what is actually present in the footage.
Review timestamped rows, jump to specific moments, export to JSON. Dense structured text that LLMs and downstream systems can parse, search, and reason over without watching the footage.
The format is designed to retain structural and contextual fidelity. Frame-by-frame delta representation captures visual changes incrementally. Word-level transcription preserves timing and speaker context. A dedicated miscellaneous field retains information that does not fit cleanly into visual or audio columns. Output quality can be verified directly by uploading your own footage.
Use structured video text instead of raw footage. Power search, analysis, and reasoning workflows with a canonical text layer for video content.