CASE STUDYResearch-Led AI Discovery
INTRODUCTION
Scaling UX research so decisions start with insightS not assumption.
This project explored how a digital platform can bring complex financial market information into a single, accessible experience. These platforms provide live and historical data across asset classes such as equities, fixed income, currencies, indices, and rates to support informed investment and trading decisions.
We reimagined search within a financial information platform as a proof of concept for AI-powered discovery and instrument comparison. The work focused on improving how users find, compare, and interpret market data in a high-stakes environment where accuracy, trust, and clarity are essential. A strong emphasis was placed on research to ensure that new and evolving financial products are grounded in real user needs and support sound decision-making.
Goals
Understand how investors and financial professionals search for and evaluate market information.
Identify gaps and friction in existing financial data discovery workflows.
Ensure product direction was grounded in rigorous user research and domain constraints.
Enable the use of AI within workflows in a meaningful way.
IMpact
Conducted user research to validate real behaviors, needs, and pain points across complex information workflows.
Designed and tested rapid prototypes to explore new experiences and interactions.
Defined a clear roadmap for evolving discovery and search capabilities within a data-rich platform.