Design a health insurance recommendation system that analyzes users' healthcare usage history to suggest the best plans. By evaluating past medical expenses, treatment patterns, and coverage needs, the system provides personalized suggestions that optimize cost and benefits. Using data analytics and machine learning, it identifies trends in healthcare utilization and predicts future needs, ensuring tailored recommendations. The system compares available insurance plans, highlighting those that offer the most value based on the user’s specific requirements, such as premium affordability, coverage limits, and network preferences, enabling informed decisions for better healthcare management.

Our idea addresses energy inefficiencies in government entities by leveraging advanced technologies to reduce waste and promote sustainability. Using the GROK model integrated with a Retrieval-Augmented Generation (RAG) system and FAISS vector database, we analyze large datasets to identify energy waste areas quickly and effectively. The system provides actionable strategies to optimize energy consumption, such as upgrading equipment, adopting renewable energy, and reducing resource underutilization. A user-friendly interface built with Streamlit enables seamless interaction, allowing government departments to make data-driven decisions. This solution not only reduces energy costs by at least 25% but also ensures better resource allocation and significant environmental benefits.