A Generative AI-Driven Legal Assistant for Women Empowerment with Government Scheme Recommendation

Access to understandable legal information and relevant welfare programmes can be difficult when information is distributed across legal documents, government portals and service providers. This paper presents a prototype of a Generative AI-driven legal assistant intended to help women and other applicants explore legal information, understand uploaded documents and discover potentially relevant government schemes through a single web interface. The design combines a frontend, a Flask/FastAPI-style backend, a database, a retrieval-augmented generation (RAG) component, a document-processing module and a scheme-recommendation module. User queries are validated and classified; relevant material is retrieved from a curated legal and government knowledge base; and the language model prepares a plain-language response with source-oriented guidance. Scheme recommendations are matched against profile details and eligibility conditions, while uploaded documents can be summarized to support comprehension. The supplied project artefacts include architecture, use-case, sequence, activity and data-flow diagrams, together with interface screenshots for scheme discovery, application tracking and emergency-support information. These artefacts demonstrate the prototype workflow and interface coverage, but do not establish measured legal accuracy, recommendation precision, response latency or user impact. The paper therefore reports a design-and-prototype contribution and defines a validation plan rather than claiming unmeasured performance. Safety measures include official-source prioritization, visible limitations, privacy-aware document handling and referral to qualified legal or emergency services where appropriate