RetailGenie – AI-Powered Retail Management & Automation System

Registration ID: IJNRD_322496

Published ID: IJNRD2604022

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Nayan Jain, Atamprakash Munjwani, Nikita Namdeo, Ajeet Shukla, Allen Masih

AI-powered retail, Retail automation, Inventory management, Sales data processing, Backend system, Python, Shell scripting, Bash automation, Scalable architecture, JSON data storage, Database integration, MySQL, MongoDB, Cloud computing, Sales forecasting, Customer behavior analysis, Recommendation system, Anomaly detection, Real-time analytics, Modular design, POS system, Retail technology, Data-driven decision making, Small and medium enterprises (SMEs)

RetailGenie is an AI-powered retail management and automation system designed to transform traditional retail operations through intelligent backend processing, automation, and scalable data management. The retail sector continues to rely heavily on manual inventory tracking, repetitive data entry, and fragmented sales monitoring systems. These outdated practices often result in operational inefficiencies, human errors, and delays in business decision-making. RetailGenie aims to overcome these challenges by introducing a structured, modular, and automated backend framework that enhances efficiency while remaining adaptable for future technological advancements. The system is built using Python as its core backend technology, enabling robust handling of essential retail operations such as inventory updates, sales data processing, and automated report generation. Python’s flexibility and extensive library support make it well-suited for managing complex data workflows and integrating future AI capabilities. In addition to the backend logic, RetailGenie incorporates Shell and Bash scripting to automate key development and deployment tasks, including environment setup, project initialization, system cleanup, and readiness checks. This automation significantly reduces manual workload for developers and ensures consistency, reliability, and errorfree execution across multiple environments. A key strength of RetailGenie lies in its scalable and AI-ready architecture. The system is designed in a modular manner, allowing new features to be integrated seamlessly without disrupting the core functionality. This design enables future expansion into advanced AI-driven modules such as sales forecasting, customer behavior analysis, personalized recommendation systems, and anomaly detection. By preparing the system for these enhancements, RetailGenie not only addresses current retail challenges but also positions itself as a forward-looking solution aligned with emerging industry trends. For data handling, RetailGenie employs structured JSON-based storage, which supports rapid prototyping and efficient data exchange. This approach allows developers to quickly build, test, and modify system components. Furthermore, the system is designed with the flexibility to migrate to more scalable database solutions such as MySQL, MongoDB, or cloud-based databases as business requirements grow. This ensures that RetailGenie can evolve from a lightweight system for small businesses into a robust platform capable of handling large-scale retail operations. By automating repetitive processes and organizing backend operations into clean, maintainable modules, RetailGenie significantly enhances operational efficiency and data accuracy. It enables retailers to access real-time insights, improve inventory management, and make informed decisions quickly. The system is particularly beneficial for small and medium-sized enterprises seeking to reduce manual effort, minimize errors, and transition toward intelligent retail systems. In conclusion, RetailGenie represents a modern and extensible approach to retail management. Its combination of automation, modular architecture, and AI readiness makes it a strong foundation for future developments, including full-scale point-of-sale systems, cloud-integrated platforms, and advanced analytics solutions. Through its innovative design, RetailGenie contributes to the evolution of retail technology by bridging the gap between traditional practices and intelligent, data-driven operations

Published Paper   E-Certificate

Nayan Jain, Atamprakash Munjwani, Nikita Namdeo, Ajeet Shukla, & Allen Masih (April-2026). RetailGenie – AI-Powered Retail Management & Automation System. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), a210-a214. https://ijnrd.org/papers/IJNRD2604022.pdf

Volume 11 Issue 4, April-2026

Pages : a210-a214

Paper Reg. ID: IJNRD_322496

Published Paper Id: IJNRD2604022

Research Area: Other area not in list

Author Type: Indian Author

Country: Begumganj, Madhya Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2604022.pdf

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