Asset Intelligence (DAM Auto-Labeling)
Automatically label, tag, and organize digital assets in your DAM. Extract visual, audio, and text features to make every asset instantly searchable and properly categorized.
Creative teams, brand managers, and media companies managing 100K+ digital assets across DAM platforms
Digital asset managers spend hours manually tagging uploads. Assets are inconsistently labeled, hard to find, and frequently re-created because teams can't discover existing assets. As libraries grow, the tagging backlog becomes unmanageable.
Ready to implement?
Before & After Mixpeek
Before
Tagging workflow
Manual: 5-10 min per asset
Search hit rate
40-50% of searches find what they need
New asset visibility
2-5 days after upload
After
Tagging workflow
Automated: instant on upload
Search hit rate
90%+ of searches find what they need
New asset visibility
Immediately searchable
Tagging time per asset
100% reduction
Asset discoverability
2x improvement
Why Mixpeek
Multi-modal analysis generates richer metadata than single-mode tools. Hierarchical classification maps to your existing taxonomy. Batch processing handles backlog while real-time processing tags new uploads instantly.
Overview
Asset intelligence transforms your DAM from a storage system into an intelligent content hub. Every asset is automatically analyzed across all modalities — visual content, embedded text, audio tracks, document content — and tagged with rich, consistent metadata. Teams find what they need instantly and stop recreating assets that already exist.
Challenges This Solves
Manual Tagging Backlog
Creative teams upload faster than assets can be manually tagged and organized
Impact: 40-60% of assets have incomplete or missing metadata
Inconsistent Taxonomy
Different team members tag assets differently, using different vocabularies
Impact: Search results are unreliable and assets are effectively lost
Asset Duplication
Teams can't find existing assets and recreate them from scratch
Impact: 15-25% of creative production effort is spent recreating existing assets
Multi-Modal Blind Spots
Video and audio assets are tagged by filename or manual notes, not actual content
Impact: Rich video and audio content is effectively unsearchable
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Feature Extractors Used
Retriever Stages Used
semantic search
filter aggregate
Expected Outcomes
95% reduction
Manual tagging effort
2x improvement
Asset discoverability
70% reduction
Duplicate asset creation
Auto-Tag Your Digital Assets
Clone the DAM intelligence pipeline and connect your asset library.
Frequently Asked Questions
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Ready to Implement This Use Case?
Our team can help you get started with Asset Intelligence (DAM Auto-Labeling) in your organization.