Gen AI Driven Metadata Harvesting and Enrichment

Business Scenario

Media and content platforms handle vast amounts of video, audio, and text-based content, making efficient metadata management crucial for content discoverability, personalization, and monetization. Traditional metadata tagging methods are often manual, inconsistent, and time-consuming, leading to suboptimal content recommendations, inefficient search experiences, and missed revenue opportunities.

Challenge

Solution

•   Automated Metadata Generation: AI-driven extraction of descriptive tags, summaries, sentiment analysis, speaker identity, keywords, and entity relationships for video, audio, and text content. 
•  Contextual Tagging & Advanced Clustering: Intelligent categorization using NLP insights and multimodal AI, enhancing semantic search and content discoverability. 
•  Personalized Content Discovery: AI-powered recommendations, dynamic filters, and enriched metadata to improve user engagement. 
•  Scalable and Custom AI Models: Multi-language support and adaptable AI models for seamless integration into diverse content ecosystems. 
•  Workflow Automation: AI-driven processes to accelerate metadata tagging, cataloging, and distribution.

Impact

The implementation of AI-driven metadata solution delivers:

• 20-30% increase in search relevance and accuracy. 
• 15-25% boost in watch time and click-through rates for personalized recommendations. 
• 10-20% revenue growth through improved ad targeting and content discovery. 
•  40-50% reduction in cataloging and tagging time, leading to faster content availability. 

By leveraging AI for metadata enrichment, content providers can significantly enhance user engagement, streamline operations, and unlock new revenue opportunities.

Services Rendered

• Metadata tagging 
• Hyper personalization 
• Media workflow automation 
• Content Localization 
• AI powered content recommendation