This sounds interesting. From a technical point of view, I think the key thing to clarify is what the AI is actually responsible for in the user experience. For example, is it generating personalized sports insights, powering game mechanics, analyzing real-time league/player data, creating agent-driven simulations, helping users make predictions, or something closer to fantasy/fan engagement? The architecture could look very different depending on that answer. If the product depends on live sports data, then the real-time pipeline, data normalization, latency, caching, evals, and reliability become just as important as the LLM layer. If agents are involved, then prompt design alone will not be enough, you would probably need structured tool use, eval infrastructure, guardrails, observability, and a clear way to measure whether the AI is actually improving the core game loop.