CaseReader is a law school AI study copilot that ingests your class materials (syllabus, slides, notes, past exams) and builds a per-class course brain. It then generates a professor-aligned 80+ page outline, plus personalized case briefs and auto-graded practice exams. Every rule is pin-cited to sources (via CourtListener) and the results are export-ready for Word/Google Docs with headings and formatting. A dashboard tracks coverage and weak spots so students know exactly what to study next.
CaseReader solves the core pain of law school studying: the 100–200 hours students spend building professor-specific outlines and the uncertainty of using generic, unreliable materials. By turning class files into an 80+ page professor-aligned outline with verified rules, then powering targeted briefs and auto-graded exams, we save time and remove guesswork before finals.
Launched November 16, 2025, with a full pipeline live: upload → professor-aligned 80+ page outline → briefs → auto-graded exams. First beta users at UNT Dallas Law are running full flows; pin-cites (CourtListener) and Word/Docs export are working. We’re shipping rapid updates and building a public, verified-brief library plus social content and campus-ambassador outreach to grow users.
