Smart Inventory CRUD Web Application with NLP

Abstract

Conventional inventory management workflows are constrained by manual data-entry overhead, limited analytical depth, and the absence of intuitive human-computer interaction mechanisms. This paper proposes a Smart Inventory CRUD Web Application that embeds Natural Language Processing (NLP) and Artificial Intelligence (AI) capabilities into a full-stack MERN architecture (MongoDB, Express.js, React.js, Node.js). The proposed framework empowers users to execute complete inventory operations—Create, Read, Update, and Delete—through conversational text commands, voice input, and a conventional graphical interface, without requiring any technical proficiency. A GROQ API-powered NLP module translates unstructured natural language queries into precisely structured database transactions. Concurrently, an AI Report Generator synthesizes raw inventory data into actionable business intelligence, encompassing Key Performance Indicators (KPIs), stock health metrics, demand trend forecasting, risk evaluations, and strategic recommendations. Extended multilingual capability spanning English, Hindi, and Marathi broadens accessibility across heterogeneous workforce demographics. The proposed architecture prioritizes modularity, cloud-native scalability, and sub-200ms operational responsiveness. This work presents the system's design rationale, architectural blueprint, AI integration strategy, expected performance benchmarks, and its contribution toward bridging the gap between enterprise-grade inventory management and natural-language-driven CRUD interfaces.