Building Intelligent Audio and Video Metadata Systems with AI and Machine Learning
The Challenge
An AI start up licensing multi-media content to generative AI companies managing a catalogue of tens of thousands of audio and video assets needed a smarter way to catalog, enrich, and retrieve content at scale. Manual tagging was no longer feasible, and traditional metadata management tools couldn't keep pace with the growing library. Thynker was engaged to design and advise on an AI-driven metadata intelligence system — combining automation, human-in-the-loop workflows, and new standards for multi-modal content understanding.
Our Approach
Thynker provided strategic consulting and technical implementation across two major initiatives: AI-driven audio cataloging and a framework for next-generation video metadata.
- •Used machine learning models and audio embeddings to automatically analyze and tag tens of thousands of tracks
- •Developed a cross-referencing engine to correlate stems and masters, ensuring every derivative asset could be traced back to its origin
- •Implemented automated metadata enrichment, extracting BPM, genre, tonal key, and rhythm structure directly from audio files
- •Created a human-in-the-loop validation system, enabling experts to review AI-generated tags and continuously improve accuracy
- •Designed a backend and API delivery layer to make the enriched catalog searchable by attribute, similarity, or audio embedding
- •Advised on the architecture and standards for annotating and describing video content using AI
- •Developed a taxonomy and metadata schema defining how video could be analyzed by frame, scene, or second, depending on context and use case
- •Explored the use of multi-modal foundation models to generate intelligent, contextual descriptions of visual content
- •Laid the groundwork for automated, explainable metadata pipelines that could support search, training data creation, and AI-based recommendation systems
The Results
- ✓Automated the cataloguing of tens of thousands of audio files, saving an estimated 20,000+ hours of manual tagging work
- ✓Achieved high-precision metadata enrichment, improving discoverability and licensing workflows
- ✓Introduced a standardized video metadata framework that bridges human creative understanding and AI interpretation
- ✓Created a scalable API-based infrastructure for both audio and video assets — ready for integration into future Gen-AI pipelines
Thynker helped us solve a problem we didn't even know how to define. Their AI-driven metadata system not only automated our audio cataloging but also created a blueprint for how we approach video content. The work they did is foundational to our entire business model.
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