Prae8 Legal Search

The Challenge

Legal research is time-intensive and expensive for small practices. Traditional legal databases are costly and often overwhelming, requiring extensive training to use effectively.

Small law firms and solo practitioners need an affordable, intuitive way to search through legal documents and find relevant case law, statutes, and legal precedents quickly.

Current Status

This is an active project in development. I'm building an AI-powered search engine for legal documents that makes legal research more accessible and affordable.

The system uses modern AI techniques to understand legal queries and return relevant documents with proper context and citations.

Technical Approach

The system is built using a Retrieval-Augmented Generation (RAG) pipeline that combines document retrieval with AI-powered analysis:

• RAG pipeline for intelligent document retrieval and response generation
• ChromaDB for efficient vector storage and similarity search
• HuggingFace embeddings for semantic understanding of legal text
• FastAPI backend for scalable API architecture
• Next.js frontend for responsive user interface

The RAG approach ensures that responses are grounded in actual legal documents while providing natural language explanations.

Technologies

ChromaDB provides efficient vector search capabilities, while HuggingFace models enable semantic understanding of legal language and concepts.

What I'm Learning

This project is pushing my understanding of AI/ML applications in domain-specific contexts. Legal text has unique characteristics that require careful consideration in embedding and retrieval strategies.

I'm exploring how to balance accuracy with accessibility, ensuring the system provides reliable legal information while remaining usable for practitioners with varying technical backgrounds.