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

早期评估创业公司是一个复杂的任务，需要专家进行详细的分析。本文介绍了创业成功预测框架 (SSFF)，这是一个结合传统机器学习和先进语言模型的新型自动化系统。SSFF 由预测模块、分析师模块和外部知识模块组成，以模拟风险投资家的分析过程，并从外部来源获取实时信息，自动化地进行高精度的分析。