AI-Powered Digital Asset Management
Transform your DAM with AI-powered search, auto-tagging, and duplicate detection. Find any asset by describing what you need rather than guessing filenames.
Media companies, creative agencies, brand teams, and publishers managing libraries of 500K+ images, videos, and documents across production workflows
Media libraries grow faster than teams can organize them. Filename-based search fails because assets are poorly named. Metadata is incomplete or inconsistent. Teams waste hours hunting for assets they know exist somewhere in the library, and frequently recreate assets because the originals are unfindable.
Ready to implement?
Why Mixpeek
True semantic search understands visual and conceptual content, not just filename keywords. Batch processing indexes existing libraries while real-time processing handles new uploads. Near-duplicate detection reduces storage costs and prevents version confusion.
Overview
AI-powered digital asset management makes every asset in your library instantly findable through natural language search. By extracting rich multimodal metadata from images, videos, and documents, Mixpeek eliminates the gap between what teams need and what keyword search can deliver.
Challenges This Solves
Asset Discovery Failure
Creative teams cannot find existing assets because search depends on manually assigned filenames and tags that are inconsistent or missing
Impact: Teams spend 30+ minutes per search session and recreate 15-20% of assets that already exist in the library
Metadata Inconsistency
Different team members, departments, and agencies apply different naming conventions and tag vocabularies to the same types of assets
Impact: Search results vary based on who tagged the asset, not what the asset contains
Video and Audio Opacity
Rich media assets like video footage and audio recordings cannot be searched by their actual content, only by external metadata
Impact: Specific shots, scenes, and audio segments are invisible to search, making video libraries particularly underutilized
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Feature Extractors Used
multimodal extractor
text extractor
course content extractor
Retriever Stages Used
feature-search
attribute-filter
rerank
Rerank documents using cross-encoder models for accurate relevance
Expected Outcomes
80% faster search-to-find
Asset discovery time
2.5x more assets reused vs. recreated
Asset reuse rate
95% of assets fully tagged automatically
Metadata completeness
Add AI Search to Your Media Library
Clone the DAM intelligence pipeline and connect your storage via S3 or compatible object storage.
Frequently Asked Questions
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Ready to Implement This Use Case?
Our team can help you get started with AI-Powered Digital Asset Management in your organization.