The client was building a legal assistance service focused on Israeli law, primarily around repatriation and housing-related questions. The target audience included people preparing to move to Israel, as well as legal professionals who regularly handle repetitive consultations.

Legal advice in this area is expensive and often inaccessible. At the same time, public sources such as forums and social media rarely provide answers that fit a person’s specific situation.

Many legal questions cannot be answered with a simple search. Users often describe their situation in free form, combining personal context, documents and edge cases.

In Israel, this is further complicated by the common law system, where legal outcomes depend not only on statutory texts but also on precedents. Even when regulations exist, relevant information is scattered across multiple documents and interpretations.

A standalone language model cannot be trusted with legal guidance:

• It may lack up-to-date or jurisdiction-specific knowledge
• It can hallucinate answers that sound convincing but are incorrect
• It cannot reliably distinguish relevant legal context without guidance

To be useful, the system needed a way to ground its answers in verified legal sources, rather than relying on general language model knowledge.

We designed a legal assistant based on a retrieval-augmented generation (RAG) approach.

Before exposing the system to users, the client provided a curated set of Israeli legal materials covering repatriation and housing regulations. These documents were processed, structured and stored in a searchable knowledge base.

When a user submitted a question:

The result was a conversational interface that simulated the flow of a legal consultation, while remaining anchored to verified legal texts.

The system was designed to support two scenarios:

• End users, seeking preliminary guidance before deciding whether a full legal consultation was required
• Legal professionals, using the assistant to speed up routine consultations and information lookup

In both cases, the goal was not to replace lawyers, but to reduce repetitive work and improve access to basic legal clarity.

The legal assistant was fully implemented and functionally operational, covering the complete consultation flow — from user context interpretation to source-grounded response generation.

The project validated the use of retrieval-augmented AI for legal guidance, demonstrating how curated domain knowledge can be combined with natural language interaction to deliver clear, explainable answers in a sensitive domain.