Generative Artificial Intelligence (GenAI) is revolutionizing the business world by
increasing availability, efficiency, cost reduction, and innovation. This paper explores the
application of Large Language Models (LLMs) and GenAI to finance. It proposes a novel
framework on how we can imagine robo-advisory systems, from a traditional rigid platform
to a more humanized solution that further engages the investor in a hand-picking asset
selection process and better understands their goals and profile using LLMs. We designed
an end-to-end solution to overcome many limitations such as lack of flexibility in robo-
advisors, lack of possible asset types (usually only equities) and the problem of real-time
access to high quality data. The solution architecture includes dynamic client profiling, risk
aversion estimation and portfolio optimization. Using robust data pipelines to curate the
latest market information, the Asset Selector Agent has been customized. Through iterative
development, we employed prompt engineering and multi-agent workflows to enhance user
interactions and deliver meaningful insights. By developing an innovative chatbot platform,
we demonstrate the potential of LLMs to transform customer service, increase engagement,
and provide strategic financial advice