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@article{190572,
        author = {Prathmesh Kulkarni and Parmesh Gupta and Mayuresh Bhandari and Ms. Seema Jamal},
        title = {VerdictAI: AI Powered Legal Advisory and Research Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {1122-1136},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190572},
        abstract = {This paper presents VerdictAI, an AI-powered legal research and advisory platform designed to automate legal research processes in India. The system integrates natural language processing, retrieval-augmented generation architecture, and locally hosted large language models to process complex legal queries across Indian Legal Acts, court judgments, and Law Commission reports. VerdictAI employs a FastAPI backend with PostgreSQL database management, Hierarchical Navigable Small World (HNSW) vector indexing for efficient similarity search, and Next.js frontend architecture. The platform enables semantic processing of legal documents, automated citation retrieval, and multi-jurisdictional document filtering with support for user-uploaded legal document analysis. Key technical innovations include hybrid retrieval mechanisms combining keyword matching with semantic understanding, multi-layer document ranking algorithms, and domain-specific prompt engineering optimized for legal reasoning. The system incorporates comprehensive privacy protection through PII redaction middleware and maintains data sovereignty via local LLM deployment using Ollama. Performance evaluation demonstrates sub-second retrieval times and accurate legal analysis across diverse query types. Future enhancements include cross-jurisdictional case filing integration, specialized legal domain extensions, and multilingual support for Indian regional languages, establishing a robust technological foundation for modern legal practice in India.},
        keywords = {artificial intelligence, legal research, natural language processing, retrieval-augmented generation, vector database},
        month = {January},
        }