Shopping advisor AI

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Inspiration

Online shopping can be overwhelming because users have thousands of product choices with different prices, ratings, and reviews. Many people struggle to decide which product is actually the best for their needs and budget. We wanted to build an AI assistant that simplifies shopping by understanding what the user wants and recommending the best options quickly. The idea was to create a smart shopping advisor that acts like a personal assistant for online purchases.

What it does

Shopping Advisor AI helps users find the best products based on their requirements. Users simply describe what they want in natural language, such as a budget, product type, or preferred features.

The system then: Understands the user’s request Finds relevant products Compares options based on price, ratings, and features Recommends the best choices to the user This makes online shopping faster, easier, and more personalized. How we built it The project was built using a simple AI-powered architecture. Frontend: Python with Streamlit to create an interactive user interface Backend: Python for processing user queries AI Model: Amazon Nova to understand user requests and generate intelligent recommendations Product Data Processing: The system analyzes product information such as ratings, price, and features to recommend the best options The application allows users to enter their shopping request and receive curated product suggestions instantly. Challenges we ran into One of the main challenges was designing an AI workflow that could correctly interpret user intent from natural language. Another challenge was structuring the product comparison logic so that the AI could provide meaningful recommendations rather than random suggestions. We also had to ensure the system remained simple and fast enough for a hackathon prototype. Accomplishments that we're proud of We successfully created a working AI-powered shopping advisor that can understand user needs and provide helpful product recommendations. The system demonstrates how AI can simplify online shopping by acting as a personal shopping assistant. We are proud that we were able to design a functional prototype within a limited hackathon timeframe. What we learned During this project we learned how to: Integrate AI models into real-world applications Design an AI agent that interacts with users Build quick prototypes using Streamlit Structure a system that combines AI reasoning with practical decision-making We also gained valuable experience working with modern AI development tools. What's next for Shopping Advisor AI In the future we want to expand the system with more advanced features such as: Real-time product data integration Price tracking and deal alerts Personalized recommendations based on user preferences Voice-based shopping assistant Full integration with e-commerce platforms Our goal is to turn Shopping Advisor AI into a complete intelligent shopping companion.