Shot-Level Video Asset Management

01

A file can hide dozens of usable shots

One camera card, reference reel, or generated video can contain many moments worth reusing. If the library only knows the filename, every later search turns into scrubbing.

• A single video may contain multiple scenes, angles, product details, gestures, and transitions.
• The useful moment may be only a few seconds long.
• Shot-level indexing lets each reusable moment become a searchable unit.

02

Shot-level search matches how creators remember footage

Creators rarely remember the exact filename. They remember what was on screen: the subject, motion, lens feel, scene, color, or camera move.

• Search for subject and action, such as a hand lifting a product.
• Search for scene and light, such as a rainy neon street or bright kitchen close-up.
• Search for motion and mood, such as slow push-in, handheld walk, calm, tense, or futuristic.

03

How ShotMind builds the layer

ShotMind splits imported local videos into shots. You pick which shot clips are worth cloud AI analysis, and the returned descriptions, thumbnails, and local searchable index stay with your local library.

• You do not need to analyze every file at once.
• Already-analyzed shots can be browsed and searched locally.
• AI descriptions are a discovery layer, so important results should still be checked against the original footage.

04

How it works with existing tools

Shot-level management is a layer before editing and file organization, not a replacement for them. Keep folders, editors, and reference libraries; use ShotMind when the missing piece is the moment inside the video.

• Use folders for project structure and source ownership.
• Use Eagle or other libraries for image-heavy references.
• Use ShotMind when the question is which shot inside the video matches the idea.