Introduction  Venture capital (VC) investment decisions hinge on anticipating the success of startups that typically operate in uncertain, data-scarce environments. Early-stage startups present noisy, limited data, making it difficult to accurately evaluate their potential. Traditional methods relying on manual due diligence or standard machine learning models often fail to capture the subtle signals embedded in unstructured data such as founder backgrounds, market narratives, or evolving technology trends.  The recent integration of large language models (LLMs) into feature engineering has transformed this landscape by enabling automated extraction of rich, multi-dimensional features from unstructured textual data. A layered ensemble of machine learning…

Need help promoting your event and finding the right target audience? Then contact us today.