Jurist.ma / Legal research and document analysis

Legal research that shows its sources.

An AI-assisted research and document-analysis platform centred on Moroccan legal context and reviewable sources.

Delivered system

The problem

A fluent answer is not enough when the evidence cannot be checked or private documents need a clear boundary.

The key decision

Treat retrieval, source context, document privacy, and limitations as part of the product experience.

What we built

Make the work clearer and easier to hand off.

  • An AI-assisted legal research interface for questions about Moroccan legal topics
  • Structured retrieval from relevant legal material before a response is produced
  • Citation-supported answers that give users a path back to the underlying material
  • Private document upload, analysis, saved conversations, access controls, and subscription support

Before the project

  • General-purpose answers could sound certain without a controlled Moroccan legal source base.
  • Users had no dependable route from an answer back to material they could inspect.
  • Private document analysis needed a boundary from public legal knowledge.

A legal question needs more than a polished reply.

Jurist.ma was conceived as an AI-assisted legal research and document-analysis platform focused on Moroccan law. The aim was not to make a chatbot sound convincing. It was to help lawyers, companies, entrepreneurs, students, and citizens reach relevant material with enough context to review it properly.

That distinction matters. General AI systems can answer confidently without a controlled knowledge base, reliable sources, or a clear boundary between information and formal advice. In legal work, those gaps can make an answer less useful precisely when it sounds most certain.

Retrieval and interface design had to work together.

The product needed to organise legal material so the right context could be found before a response was generated. It also needed to present that context in a way that did not overwhelm the person asking the question.

Quindart designed the research flow around a simple sequence: ask a question, retrieve relevant material, produce a structured response, and show the sources that are available for review. The interface supports exploration, while the source trail gives the user a practical way to test what they are reading.

Document analysis needed a separate trust boundary.

When a user uploads a document, the platform processes it so they can ask about clauses, sections, and meaning in context. That private document context remains separate from the public legal knowledge base.

This design recognises that uploaded legal material may be sensitive. It also makes the product's role clearer: it helps users inspect and understand information, but it does not replace the professional review or advice that a legal matter may require.

The working process

  1. A user asks a question or uploads a document for review
  2. The platform finds relevant material from the appropriate context
  3. The response is generated with that context and available sources are surfaced
  4. The user reviews the information and remains responsible for professional judgment

What changed

  • Faster access to relevant legal information and topics
  • A more transparent way to explore answers than a general purpose chatbot provides
  • A useful first pass for long documents without treating the result as legal advice
  • A searchable product experience built around an existing legal content library

Systems involved

Legal knowledge base · Source retrieval · Document analysis · User accounts

The lesson

Trust is designed into the route from question to evidence; it cannot be added after the interface is finished.

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