Genie

Genie-Logo

Description:
Genie creates personalized crypto portfolios that run on autopilot. Genie’s cutting-edge AI optimizes and manages portfolios based on end-user investment preferences, risk tolerance, and market conditions. Their mission is to enable smarter, effortless digital assets investing for everyone.

Differentiator:

Currently, digital asset trading requires analytical tools, information access, and investment experience that is not accessible to retail investors. Genie’s AI models continuously track granular market data, news, and social media sentiment to identify signals and trigger trades, improving over time and removing pain and stress from the investing experience.

Opportunity:
There are retail platforms that allow individuals to buy/sell crypto, passive BTC/ETH baskets, DeFi protocols, and automated trading bots, but no solutions give personalized exposure to crypto upside with low downside risk. Genie uses advanced AI models typically deployed by sophisticated institutional investors and makes them available to anyone, regardless of their net worth or education level, through an effortless user experience.
 
Customers:
Genie has solutions for individuals and for businesses. Genie’s customer base is unlimited—anyone who wants to create a portfolio through Genie’s platform can.

Team:

Federico Mele, Genie CEO, focuses on building the company’s team and business partnerships. He has served as a VC investor for a top-performing data-driven fund based in New York and has focused his career on investing in private companies at the venture and growth stages. Federico holds an MBA in Finance from Wharton and a BA & MA in Economics from Boston University.

Lorenzo Gentile, Genie CTO, focuses on building the AI models and decision-making engines powering the product. Lorenzo has a PhD in Machine Learning and Optimization models from the University of Strathclyde, UK. Additionally, Lorenzo holds a BS & MS in Aerospace Engineering from Politecnico di Torino, one of Europe’s top engineering programs. During his PhD, Lorenzo won several international awards for his optimization models applied to stochastic systems.

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