Agentic generative AI for media content discovery at the national football league
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909831624916992 |
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| author | Wang, Henry Salekin, Md Sirajus Lee, Jake Claytor, Ross Zhang, Shinan Chi, Michael |
| author_facet | Wang, Henry Salekin, Md Sirajus Lee, Jake Claytor, Ross Zhang, Shinan Chi, Michael |
| contents | Generative AI has unlocked new possibilities in content discovery and management. Through collaboration with the National Football League (NFL), we demonstrate how a generative-AI based workflow enables media researchers and analysts to query relevant historical plays using natural language rather than traditional filter-and-click interfaces. The agentic workflow takes a user query as input, breaks it into elements, and translates them into the underlying database query language. Accuracy and latency are further improved through carefully designed semantic caching. The solution achieves over 95 percent accuracy and reduces the average time to find relevant videos from 10 minutes to 30 seconds, significantly increasing the NFL's operational efficiency and allowing users to focus on producing creative content and engaging storylines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic generative AI for media content discovery at the national football league Wang, Henry Salekin, Md Sirajus Lee, Jake Claytor, Ross Zhang, Shinan Chi, Michael Artificial Intelligence Generative AI has unlocked new possibilities in content discovery and management. Through collaboration with the National Football League (NFL), we demonstrate how a generative-AI based workflow enables media researchers and analysts to query relevant historical plays using natural language rather than traditional filter-and-click interfaces. The agentic workflow takes a user query as input, breaks it into elements, and translates them into the underlying database query language. Accuracy and latency are further improved through carefully designed semantic caching. The solution achieves over 95 percent accuracy and reduces the average time to find relevant videos from 10 minutes to 30 seconds, significantly increasing the NFL's operational efficiency and allowing users to focus on producing creative content and engaging storylines. |
| title | Agentic generative AI for media content discovery at the national football league |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.07297 |