In this demo, we present a prototype tool designed to help financial advisors recommend private pension funds to investors based on their preferences, offering personalized investment suggestions. The tool leverages Large Language Models (LLMs), which enhance explicability by providing clear and understandable rationales for recommendations and effectively handles both sequential and cold-start scenarios. We outline the design, implementation, and results of a user-based evaluation using real-world data. The evaluation shows a high recommendation acceptance rate among financial advisors, highlighting the tool’s potential to improve decision-making in financial advisory services.