NextGen Dynamic Pricing

In this fast-evolving age of e-commerce, fixed price strategies are not effective enough to compete and make profits. The paper proposes an AI-driven Dynamic Pricing Engine, created to automate and optimize products' prices at online stores. The platform integrates a base price forecasting model of XGBoost regression with real-time price checking of Snapdeal, both supported by a layer of business logic that optimizes prices in real time, considering products' ratings, stocks' availability, customers' interest, discounts, and seasonal fluctuation of market demand. The architecture involves a Flask-based Python back-end, which includes automated price updates every 30 minutes, real-time data synchronization with a Firebase Realtime Database, which in turn powers a live website user interface. Using a purpose-built web scraper with BeautifulSoup, the engine continuously monitors opponents' prices to remain both competitive and financially sustainable. This hybrid design pits recommendations based on AI against pre-programmed business rules, delivering a more robust solution than hand tuning by itself or machine learning. The deployment, with fallbacks such as Postman, also demonstrates in low-resource settings that it might be possible to scale and sustain a savvy price infrastructure that in real time adjusts as conditions in the market fluctuate.