One of the biggest challenges in this role was understanding the diverse use cases within the legal domain. Coming in without a legal background, I relied heavily on user feedback to identify pain points and uncover opportunities for automation that lawyers would actually adopt. What quickly became apparent was that the effectiveness of any solution varied greatly depending on the size of the law firm, the nature of their work, user roles, and internal workflows, meaning a one-size-fits-all approach wouldn’t work. During user research, we uncovered a strong need for a visual mapping tool. Lawyers were spending significant time manually creating diagrams for client reports, a tedious task they disliked, but one that added high value for clients and was often considered essential. We built a tool that automated this process, reducing effort while maintaining the quality clients expected. To further improve efficiency, we introduced an AI-powered document review feature. Users could upload PDFs, receive flagged sections based on severity, and ask natural-language queries to quickly extract key information. A split-view interface allowed them to compare the original document with AI-generated insights side by side, helping them validate results while saving time and gaining context without reading through lengthy legal documents.