An Automated Startup Evaluation Pipeline: Startup Success Forecasting Framework (SSFF)
<|reference_start|>An Automated Startup Evaluation Pipeline: Startup Success Forecasting Framework (SSFF): Evaluating startups in their early stages is a complex task that requires detailed analysis by experts. While automating this process on a large scale can significantly impact businesses, the inherent complexity poses challenges. This paper addresses this challenge by introducing the Startup Success Forecasting Framework (SSFF), a new automated system that combines traditional machine learning with advanced language models. This intelligent agent-based architecture is designed to reason, act, synthesize, and decide like a venture capitalist to perform the analysis end-to-end. The SSFF is made up of three main parts: - Prediction Block: Uses random forests and neural networks to make predictions. - Analyst Block: Simulates VC analysis scenario and uses SOTA prompting techniques - External Knowledge Block: Gathers real-time information from external sources. This framework requires minimal input data about the founder and startup description, enhances it with additional data from external resources, and performs a detailed analysis with high accuracy, all in an automated manner<|reference_end|>
@article{wang2024an,
    title={An Automated Startup Evaluation Pipeline: Startup Success Forecasting
  Framework (SSFF)},
    author={Xisen Wang, Yigit Ihlamur},
    journal={arXiv preprint arXiv:2405.19456},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19456},
    primaryClass={cs.AI}
}