AI video tagging
Manual tags work until the library grows. ShotMind uses AI to describe selected shot clips, so creators can search by what is visible instead of maintaining every tag by hand.
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Manual tags are useful when a library is small and everyone uses the same language. They become slow and inconsistent when footage comes from many projects, cameras, exports, or AI generation tests.
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ShotMind analyzes the shot clips you choose and creates searchable descriptions. The goal is not to replace your judgment, but to make retrieval faster than manual tagging alone.
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Think of AI descriptions as a layer above your folders, not a command to reorganize everything. Your files can stay where they are while ShotMind helps you find visual moments.
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AI can miss details or misunderstand context. It is strongest as a fast retrieval layer, not as an authority on meaning, rights, or final creative judgment.
"symmetrical wide shot, centered subject, cool blue background"
Find visual structure that would be hard to tag manually.
"slow push-in on product detail with shallow depth of field"
Search by motion and framing, not only object names.
"tense low-light dialogue shot, over-the-shoulder framing"
Find mood and staging cues across different files.
No. ShotMind is selective. You import local videos, split them into shots, and choose the shot clips worth analyzing. Those analyzed clips become searchable.
They can reduce a lot of manual tagging work, but they should not replace human judgment. Use AI descriptions to search faster, then verify important matches.
It can help with visible subjects, actions, scenes, composition, lighting, mood, style, and camera movement, depending on the shot and analysis quality.