AI Scene Analysis → Automated Metadata Tagging → Asset Organization in Dropbox
Analyze video footage with AI to automatically generate metadata tags and organize assets into structured folders for easy retrieval by production teams.
Workflow Steps
Runway ML
AI scene and content analysis
Process raw footage through Runway ML's video analysis tools to identify scenes, objects, people, locations, and key moments. Extract metadata including shot types, lighting conditions, and content themes.
Zapier
Automated tagging workflow
Create a Zapier automation that triggers when new analysis data is available from Runway ML. Use the extracted metadata to automatically generate consistent tags and file naming conventions based on your production standards.
Airtable
Metadata database creation
Store all video metadata in an Airtable base with fields for scene type, duration, characters, locations, and custom tags. Create filtered views for quick asset discovery and link to actual video files.
Dropbox
Automated file organization
Use Dropbox's API integration to automatically sort video files into structured folders based on the generated tags. Create folder hierarchies by project, scene type, character, or any custom taxonomy your team needs.
Workflow Flow
Step 1
Runway ML
AI scene and content analysis
Step 2
Zapier
Automated tagging workflow
Step 3
Airtable
Metadata database creation
Step 4
Dropbox
Automated file organization
Why This Works
Eliminates manual tagging and filing of video assets while creating a searchable database that scales with your production volume, making it easy for editors to find specific shots quickly.
Best For
Production companies managing large volumes of video footage and needing efficient asset organization
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