Inspiration
Legal documents are often difficult for students, freelancers, and small businesses to understand due to complex legal language. Many people sign agreements without realizing hidden risks or missing protections. This project was inspired by the need to make legal information more accessible and easier to understand for non-lawyers.
What it does
Legal Advisor analyzes legal documents such as contracts and agreements using AI. It extracts text from PDFs or images, identifies risky clauses and missing protections, and explains everything in simple, everyday language. The platform also supports multilingual explanations and voice-based interaction for better accessibility.
How we built it
The project was built using a modern web stack. OCR is used to extract text from scanned documents, while an AI model analyzes and explains legal risks. Firebase Authentication (OAuth) handles secure user login, Firestore is used for data storage, and the frontend is built with a responsive UI for clear visualization of risks and summaries.
Challenges we ran into
Handling low-quality scanned documents was challenging, as OCR accuracy can vary. Ensuring the AI output stayed structured, simple, and non-advisory required careful prompt design. Integrating multiple services while maintaining performance and reliability was also a key challenge.
Accomplishments that we're proud of
We successfully built an end-to-end legal analysis system that converts complex legal text into understandable insights. The platform provides structured risk analysis, multilingual support, and voice explanations, making legal understanding more accessible to a wider audience.
What we learned
We learned how to design responsible AI systems, integrate OCR with AI analysis, and manage cloud-based authentication and databases. The project also taught us the importance of user-centric design, especially when dealing with sensitive and complex domains like law.
What's next for Legal Advisor
Future plans include improving OCR accuracy, adding support for more document types, expanding language coverage, and introducing advanced risk scoring and comparisons between documents. We also aim to enhance accessibility features and refine AI explanations further.
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