Outcreating Metadata Precision with Generative AI and BigQuery for a Global Media Enterprise

Our Client

The client is a global media enterprise that operates large-scale content and data ecosystems behind content discovery, personalization, and core business operations. As content volumes and data complexity grew, manual processes and reactive monitoring became harder to scale. The client needed a more consistent, automated, and governed way to manage these workflows.

Industry Trends

Media enterprises are rethinking how they manage content and data as libraries and query volumes grow. Across the industry, organizations are:

• Treating metadata as a driver of discovery and recommendations, with investment planned against measurable return
• Using data observability tools to monitor, diagnose, and resolve issues across the data lifecycle and keep pipelines reliable
• Looking beyond warehouse-level totals to the query behavior that drives cloud data costs, such as inefficient SQL and unnecessary scans
• Treating generative AI as core infrastructure, with governance, privacy, and IP protections in place before scaling it across operations

For a media enterprise operating at scale, keeping pace with these shifts depends on how well its metadata, data pipelines, and cloud workloads hold up as content volumes grow. That was where the client found itself, with manual tagging, reactive pipeline monitoring, and costly query workloads making it harder to keep up.

Key Challenges

The client’s teams encountered challenges that restricted speed, quality, and scalability. Each new asset increased the manual workload.

• Manual, inconsistent tagging: Metadata was applied by hand and inconsistently, which slowed content readiness and discovery.
• Limited anomaly detection: Without proactive monitoring, operations stayed reactive, and pipeline visibility became poor.
• Inefficient BigQuery workloads: Large-scale queries drove up compute costs and created performance bottlenecks.
• Fragmented, ungoverned workflows: Disconnected data and content processes lacked consistent automation and governance, which weakened personalization and user experience.

LTM’s Solution

LTM implemented a portfolio of AI-led solutions across content intelligence, data operations, and the engineering practices underneath both. Each solution answered one of the challenges above:

• Content intelligence: Generative AI artwork tagging generated metadata automatically. A human-in-the-loop design kept people in the process, and their feedback was used to improve tagging quality and accuracy.
• Data intelligence: Machine learning models were deployed to identify anomalies across multiple workflow areas of the data pipeline. Monitoring became proactive, and issues reached engineers sooner.
• Data engineering optimization: A query optimization framework was built for the client’s large-scale BigQuery workloads, which improved processing efficiency across those workloads and reduced data consumption.
• AI-enabled operating model: CI/CD and MLOps were implemented across all use cases. Each one followed the same deployment path and governance model, which kept delivery repeatable and gave future use cases a foundation to build on.

Business Benefits

The engagement delivered measurable improvements in content intelligence, data optimization, and operational efficiency:

• Higher-quality, scalable tagging: Precision exceeded 85% across 13 metadata categories, with tagging that scales as content volumes grow.
• Lower data costs: Data consumption fell by 30–40%, with query performance improving alongside it.
• Proactive anomaly detection: Achieved 85–90% precision in anomaly detection so pipeline issues were identified earlier.
• Reduced manual effort: Generative AI artwork tagging, combined with human-in-the-loop feedback, reduced manual metadata effort.
• Standardized workflows: CI/CD, MLOps, and automation established consistency and governance across end-to-end data workflows.
• A scalable AI foundation: Created a reusable AI-led foundation across content, data, and infrastructure that the client can keep extending.

Conclusion

AI-led content and data operations are becoming a competitive differentiator for media enterprises operating at volume. Both the metadata that drives recommendations and the data layer beneath it now run on an AI-enabled operating model. Because each capability was built for reuse, the client can bring new content, data, and infrastructure use cases onto the same foundation without starting over. With LTM as its Business Creativity partner, the client is positioned to keep its discovery and personalization accurate as its libraries and data continue to grow.

Get in Touch

For more information, write to us at info@ltm.com
Visit ltm.com/industries/communications-media-and-entertainment.