AI-Powered Stock Media Search
Transform stock media search with AI. Creatives describe their vision in natural language and find matching photos, videos, and audio across your library.
Stock media platforms, content licensing marketplaces, and enterprise media libraries serving creative professionals who need to find specific visual and audio assets quickly
Stock media search is painful. Creatives spend 30+ minutes per search session scrolling through irrelevant results because keyword tags are inconsistent, incomplete, and fail to capture the mood, composition, and style attributes that determine whether an asset fits a creative brief. Visual search would help, but most stock platforms offer only keyword filters.
Ready to implement?
Why Mixpeek
Semantic understanding matches creative intent to asset content, not just keyword overlap. Mood and composition features capture the aesthetic qualities that determine creative fit. Clustering surfaces thematic collections automatically.
Overview
AI-powered stock media search replaces keyword guessing with intent-based discovery. Creatives describe the asset they envision and the system finds it, understanding composition, mood, and style rather than relying on the inconsistent tags that make traditional stock search frustrating.
Challenges This Solves
Keyword Tag Inconsistency
Stock assets are tagged by different contributors with different vocabularies, and many assets have sparse or inaccurate keyword tags
Impact: The same search returns different results depending on which tags contributors happened to use, and relevant assets with poor tags are never surfaced
Mood and Style Inexpressibility
Creative professionals select assets based on mood, composition, lighting quality, and style, none of which are captured by standard keyword taxonomies
Impact: Creatives spend 30+ minutes per search scrolling through technically matching but aesthetically wrong results
Video and Audio Discovery Gap
Video clips and audio tracks are even harder to search than images because their content unfolds over time and cannot be assessed from a single thumbnail
Impact: Video and audio assets are dramatically underutilized compared to photos, despite growing demand for rich media
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
+45% more purchases per search session
Search-to-license conversion rate
60% reduction
Average search time to find asset
3x more rich media assets surfaced
Video and audio asset discovery
Upgrade Your Stock Media Search
Clone the stock media search pipeline and connect your asset library for semantic discovery.
Frequently Asked Questions
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Ready to Implement This Use Case?
Our team can help you get started with AI-Powered Stock Media Search in your organization.