Development of a data-driven engine for the private investment sector

近年来，在高技术部门，创业热情显著增加。因此，许多处于生命周期早期阶段的创新项目有时需要在它们所建议的价值主张和支持它们的公司结构方面进行改进。正是从这个角度来看，私人投资公司和加速器，如霍尔特发挥了关键作用。他们支持初创公司定义他们的商业模式，跟踪技术路线图和设计营销计划。然而，鉴于他们的干预的过早性和投资的高风险，需要更好地控制整个投资决策过程：从发现具有高潜力的初创公司到向这些公司分配资金。简而言之，该项目的目标是为风险投资公司开发一种解决方案，即VC即服务（或VCaaS），通过数据驱动的方法显着改善其用户的投资决策过程。除其他外，这涉及通过确定可能影响投资质量的所有因素来建立公司的肖像。这一过程中的一个重要因素是在技术和经济背景下评估初创企业潜力的质量和客观性。这需要公司专业领域的专业经验和技能。事实上，这项评估是由投资专家（称为顾问）进行的，他们是从具有不同背景的合作者中挑选出来的。因此，初创公司和顾问之间的正确匹配对于投资组合的成功至关重要。通过数据科学优化这一过程需要在信息检索系统、推荐系统、自然语言处理、监督学习以及强化学习等领域使用先进的人工智能技术。

The enthusiasm for entrepreneurship has significantly increased in recent years in the high technology sector. As a result, the many innovative projects that are in the early stages of their life cycle sometimes need refinement both in terms of the value proposition they suggest and the structure of the company that supports them. It is in this perspective that private investment firms and accelerators like Holt play a critical role. They support start-up companies in defining their business model, tracing the technological roadmap and designing a marketing plan. However, given the premature nature of their intervention and the high level of risk of the investment, better control over the entire investment decision process is required: from the discovery of startups with high potential to the allocation of funds to these companies. Briefly, what the project aims to achieve is the development of a solution for venture capital firms, VC as a Service (or VCaaS), that would significantly improve the investment decision process of its users through a data-driven approach. This involves, among other things, establishing a portrait of companies by identifying all the factors that could have an impact on the quality of the investment. One of the important factors in this process is the quality and objectivity with which a startup's potential is assessed within its technological and economic context. This requires specialized experience and skills in the field of expertise of the company. Indeed, this assessment is carried out by investment experts (referred to as advisors), selected from a pool of collaborators with diverse profiles. The right match between a startup and advisor is therefore crucial for the success of an investment portfolio. Optimizing this process through data science requires the use of advanced artificial intelligence techniques in the fields of information retrieval systems, recommendation systems, natural language processing, supervised learning, as well as reinforcement learning.