Entrepreneurial success depends on reducing uncertainty about the quality of ideas and selecting the best strategies to implement. Mentorship plays a crucial role in this process. This paper examines how mentorship improves entrepreneurial success within the Creative Destruction Lab (CDL), a global mentorship-driven startup accelerator. I investigate two key channels. First, I examine how mentors’ learning about the startup potential influences mentors’ allocation of mentorship resources. Second, I examine how mentor advice shapes entrepreneur decisions, potentially leading to better outcomes. I use mentorship interaction data from CDL, complemented with Crunchbase. I identify the types of decisions using generative AI techniques for text analysis. I use this data to estimate a dynamic structural model of incomplete information. This model captures the dynamics of mentor learning, advice implementation, and quality accumulation, enabling me to separate and quantify the value of mentorship in resolving the uncertainty around the quality of the idea and directly improving the quality itself. I use this model to conduct counterfactual analysis, simulating the effects of a policy where entrepreneurs are supported in pursuing their original plans, rather than receiving mentor-driven suggestions for alternative strategies. I estimate that the advice given by mentors is more likely to be completed, and that it is more likely to lead to successful outcomes for the startup overall, than the entrepreneur’s initial plan. I also demonstrate substantial spillovers of private quality signals between mentors. Overall, I document the mentors provide substantial value in both identifying the higher quality startups and providing those startups with strategic direction.