Automated Video Tagging for Streaming
Automate video tagging for streaming platforms. Extract scenes, objects, dialogue, mood, and genre signals to power discovery and recommendation engines.
Streaming platforms, content distributors, and VOD services managing catalogs of 10K+ titles that need rich metadata for discovery and recommendation
Streaming catalogs grow faster than editorial teams can tag. New content launches with sparse metadata, hurting discoverability. Existing titles have inconsistent tagging depth. Recommendation engines underperform because they lack the granular scene-level signals that capture why viewers engage with specific content.
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Why Mixpeek
Scene-level extraction captures the granular content signals that drive viewer engagement, not just title-level genre tags. The course content extractor decomposes long-form video into semantically coherent segments. Hierarchical classification maps to your existing content taxonomy.
Overview
Automated video tagging generates rich, scene-level metadata for every title in a streaming catalog. By extracting visual, audio, and textual features from the content itself, platforms move beyond basic genre labels to the granular signals that power effective content discovery and personalized recommendations.
Challenges This Solves
Metadata Sparsity on New Content
New titles launch with only basic metadata (title, genre, cast) because editorial tagging cannot keep pace with content acquisition
Impact: New content is poorly surfaced in search and recommendations during its critical launch window
Title-Level Granularity Limitation
Metadata describes entire titles but not the scene-level content (mood shifts, visual themes, specific sequences) that drives viewer selection
Impact: Recommendation engines rely on coarse genre and cast signals, missing the content-level nuance that predicts engagement
Inconsistent Taxonomy Application
Different editors apply the content taxonomy differently, and taxonomy evolves over time without retroactive re-tagging
Impact: Browse and filter experiences surface inconsistent results, reducing user trust in discovery tools
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Feature Extractors Used
multimodal extractor
Text Embedding
Extract semantic embeddings from documents, transcripts and text content
course content extractor
Retriever Stages Used
attribute-filter
taxonomy-enrich
rerank
Rerank documents using cross-encoder models for accurate relevance
Expected Outcomes
10x more tags than manual editorial process
Metadata tags per title
Full metadata at launch vs. weeks of editorial lag
New content discoverability
+25% with scene-level content signals
Recommendation click-through rate
85% reduction in manual tagging effort
Editorial tagging cost
Build this in the docs
The exact stages and extractors this use case runs on, with API reference and worked examples.
Auto-Tag Your Streaming Catalog
Clone the video tagging pipeline and connect your content library for automated metadata enrichment.
Frequently Asked Questions
How does automated video tagging extract metadata at the scene level?
Can automated tagging use our existing content taxonomy?
How does this improve streaming recommendation engines?
What is the processing time per title for automated video tagging?
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Ready to Implement This Use Case?
Our team can help you get started with Automated Video Tagging for Streaming in your organization.