QuickCase: The AI-Powered Legal Editing Assistant

Abstract

“Law Review” is one of the most sought after extracurriculars for students in law school. Following a competitive application process, each member serves as an editor for their respective legal journal. While prestigious, the actual work required of editors can be tedious, mundane, and repetitive. Many editors feel legal editing does not improve skills critical to their success as lawyers. Worse, the editing process can exceed tens of hours for a single assignment. The most ambitious students are incentivized to exchange their time for a more impressive resume. These students deserve to get their time back, so that they can realign their focus towards what actually matters.

QuickCase, The AI-Powered Legal Editing Assistant, transforms a week-long editing process into one that can be completed in a single sitting. The process is simple: Upload your draft to catch any Bluebook formatting mistakes, gather all referenced sources at once, and find where each source adequately supports the claims made in the manuscript. In this paper, we review current market trends in legal tech, the theory behind QuickCase’s machine learning implementation, and early user feedback from a cohort of user testers from The University of Pennsylvania Carey Law School. In addition to software iteration based on user feedback, future work will focus on marketing and product distribution, specifically targeting institutions like law schools and independent legal journals.