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With the growing emphasis on sustainability, criticality, and availability in materials research, providing actionable information about mineral commodities is crucial for informed decision-making and strategic planning by researchers, policy makers, and industry stakeholders. While the United States Geological Survey (USGS) offers valuable information on mineral-commodity summaries, their unstructured nature makes analysis challenging. To address this, we present a comprehensive data-analytics application () that processes the past 10 years of USGS mineral-commodity summaries into actionable insights. The application offers country-specific insights into global elemental production and reserves, along with quantitative metrics such as the Herfindahl-Hirschman index (HHI) to evaluate market concentration, identifying risks and opportunities in resource availability. It also features an artificial-intelligence assistant powered by a large language model (LLM) and a retrieval–augmented generation (RAG) system, enabling users to query various aspects of raw materials, including reserves, production, market share, usage, price, substitutes, recycling, and more. We evaluated multiple open-source LLMs for the RAG task and selected the best-performing model, , to implement in the system. This application provides valuable support for material scientists in assessing sustainability, criticality, and market risks, thereby aiding in the development of new materials. We demonstrate its application in energy materials, and by describing the application architecture and providing open access to the code, we aim to enable data-driven advancements in materials research.