Kive
AI creative intelligence platform that auto-organizes brand visual libraries, tags assets by content, and surfaces the right reference at the right time.
Kive is an AI-powered creative asset management platform built for in-house design teams, creative agencies, and brand studios that manage large libraries of visual references, photography, and campaign assets. The platform uses computer vision to automatically tag every uploaded image by content, color, mood, and visual style - making previously unsearchable folders of campaign photography and mood board references discoverable by natural-language search. Teams can upload brand photography, past campaign assets, and competitor or inspiration references, then retrieve exactly what they need using plain-text queries rather than browsing folders. Founded in Norway around 2020, Kive targets creative teams where the gap between having good visual assets and actually finding and using them consistently is a significant daily bottleneck. Freemium plans are available with team collaboration features on paid tiers starting around $19 per month.
Key Features
- AI auto-tagging - every uploaded image is automatically tagged by subject, color, mood, and visual style
- Natural-language visual search - find images with queries like "minimalist product photography, white background"
- Mood board creation by dragging assets from search results into shareable visual briefs
- Team library sharing with role-based access for designers, art directors, and clients
- Browser extension to save inspiration from the web directly into a personal or team library
- Brand kit integration for uploading and organizing official logo, color, and photography assets
Use Cases
- Creative agencies managing campaign photography libraries for multiple brand clients simultaneously
- In-house design teams creating consistent visual briefs by pulling from approved brand photography archives
- Art directors building mood boards from internal and external visual references before production shoots
- Brand managers ensuring designers use the correct and current brand asset versions across campaigns
Pros
- AI auto-tagging works on existing libraries - no manual metadata entry required to make old assets searchable
- Natural-language search is dramatically faster than folder browsing for teams with 10,000+ image libraries
- Browser extension makes capturing web inspiration and attributing it to a project effortless
Cons
- Not a digital asset management replacement for enterprise legal or rights-management workflows
- AI tagging accuracy on abstract or highly stylized imagery can require manual correction for precision retrieval
- Smaller teams may find the collaboration features over-engineered relative to a shared Dropbox or Notion folder
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