Rambam LawSys has been submitted to OpenAI Build Week.
The project presents a source-faithful AI workflow for legal learning. It transforms syllabi, cases, academic readings, lecture transcripts and exam materials into structured study companions, source briefs, knowledge checks and exam-oriented learning products.
The public submission includes a working demonstration, project documentation, a workflow diagram and a GitHub repository.
The central design principle is simple: source before synthesis. Rambam LawSys is designed to preserve attribution, identify uncertainty and block premature completion when required materials are missing.
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