This is like giving a football club’s scouting department a super‑assistant that has read every match report, watched all the stats, and can instantly summarize which players fit the coach’s style and why.
Traditional scouting requires a lot of manual work reading reports, watching matches, and consolidating notes across many leagues and players. This solution uses generative AI to quickly analyze large volumes of player data and text reports so scouts and coaches can focus on decisions instead of data collection.
Domain-specific scouting workflows and historical club data combined with proprietary evaluation criteria (playing style, tactical fit, budget, league constraints) baked into prompts and templates make the setup harder to copy than generic sports analytics.
Frontier Wrapper (GPT-4)
Vector Search
Medium (Integration logic)
Context window cost and latency when analyzing long scouting reports or large multi-player comparisons; also potential data privacy concerns when sending internal assessments to a managed LLM service.
Early Adopters
Focuses specifically on football scouting workflows on top of Amazon Bedrock and OpenAI models, showing how a club can operationalize LLMs around internal scouting data rather than just using generic chatbots.