Agentic generative AI for media content discovery at the national football league
This solves the problem of time-consuming video retrieval for NFL media researchers and analysts, though it is incremental as it applies existing AI methods to a specific domain.
The paper tackled the problem of inefficient media content discovery for the NFL by developing a generative-AI workflow that allows natural language queries, achieving over 95% accuracy and reducing search time from 10 minutes to 30 seconds.
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.