The process of setting up a new and successful hedge fund is an extremely complicated and multifaceted procedure, as many different and closely-related parameters have to be taken into account. Establishing a successful business plan, identifying market demand and opportunities, raising capital, and embedding a predetermined code of ethics into the core values of the firm are only but a few of the main challenges need to be addressed. In this project, we will discuss about the establishment of a fully automated artificially intelligent hedge fund, called Flow Capital Management or simply FCM. We will provide a detailed description of an innovative business plan and examine the market drivers that separate it from the rest of the industry. In doing so, we will identify and analyze modern trading models and strategies in the context of existing laws and regulatory requirements. According to Don Steinbrugge from Agecroft partners, despite the many negative articles about the hedge fund industry, hedge funds have reached an all-time high 5 quarters in a row, and the industry assets are forecasted to grow by 5.5% over the next 12 months. The strong competition led many managers to make investments in new technologies, such as artificial intelligence, advanced quantitative analytics, as well as alternative data sources. In this thesis, we will discuss how these new technologies can help FCM generate positive alpha returns, and increase the efficiency and accuracy of information analysis and trading execution. Special emphasis will be given in analyzing the proprietary trading models and strategies of our hedge fund. Finally, we will discuss about the fund structure and marketing plan of FCM and propose ways to ‘lure’ seed investments in order to raise between $100 and $150 millions of investment capital.