An AI legal-training MVP, live in about two weeks

Law students practise argumentation against AI judge personas grounded in real legal documents. We designed and shipped the production-ready MVP in roughly two weeks.

~2 weeks from start to production-ready MVP
grounded every judge answer cites its legal sources

A legal-education startup wanted to replace static reading and Q&A exercises with something closer to a real courtroom: iterative, judge-driven argument practice that scales to thousands of students.

The challenge

  • Students rarely get to practise oral argumentation in a low-stakes environment.
  • Generic chat tools cannot simulate the pressure and nuance of a judge’s questioning style.
  • In a legal context, AI hallucination is not a cosmetic problem — wrong citations make the tool unusable.

What we did

  • Built a role-based courtroom interface in React with streaming chat, where students set their case context and choose between judge personas with distinct questioning styles.
  • Integrated Gemini models through Google Vertex AI, with every judge response strictly grounded in a datastore of legal codes and rulings — judges cite their sources and admit when information is missing.
  • Kept a clean split between the React frontend, the Laravel API (including session memory for pausable, resumable arguments) and the AI layer, so new institutions can be onboarded easily.

The outcome

The production-ready MVP shipped in about two weeks. Students now engage in iterative, Socratic dialogues instead of one-off queries, and grounded generation keeps the tool safe enough for academic use.

“This platform transforms legal training from passive reading to active doing.” — product lead

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industry

Legal education

tech stack

Laravel, React, Google Vertex AI

service

Product Development

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