EX.CO introduced an agentic AI taxonomy feature that enables publishers to automate video content categorization for Ziff Davis properties. This technology utilizes Large Language Models to analyze videos and generate rich metadata, which enhances searchability and monetization of video assets. The system supports existing custom taxonomies, allowing integration with current structures. The solution distinguishes itself by complementing legacy taxonomies rather than replacing them, ensuring institutions can leverage automation without losing control over their editorial processes.
EX.CO's AI taxonomy solution has allowed us to deeply tag our extensive video library with a fraction of the necessary time. This gives the product team confidence in the recommendations being powered by this new technology.
Unlike legacy systems that rely on time-consuming manual tagging, EX.CO's taxonomy solution uses Large Language Models (LLMs) to analyse video content frame by frame-generating rich metadata across more than 30 dimensions, including IAB categories, sentiment, tone, safety classification, and contextual relevance.
Whether starting from scratch or enhancing an existing framework, EX.CO's flexible architecture ensures an easy path to adopting LLM and agentic AI technologies-without sacrificing control.
What sets EX.CO's solution apart from broader contextual platforms is its ability to complement, not replace, existing editorial taxonomies-bridging legacy systems with next-gen automation, and translating that structure into real monetisation opportunities.
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