PurePicks

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Inspiration

Consumers are increasingly overwhelmed by misleading marketing claims, complex ingredient lists, and inconsistent product information. We wanted to build a tool that helps people make informed purchasing decisions instantly. The idea for PurePicks came from the frustration of standing in a store and not knowing whether a product truly matched personal preferences, dietary needs, or sustainability goals.

What it does

PurePicks is an AI-powered product analysis platform that allows users to scan or search products and receive clear, personalized insights. The application analyzes ingredients, product details, and user preferences to provide recommendations, identify potential concerns, and help users make smarter purchasing decisions. Users can maintain a profile, track scan history, and receive recommendations tailored to their needs.

How we built it

We built PurePicks using: -React and TanStack Start for the frontend and routing -TypeScript for type safety -Supabase for database storage, and backend services -AI-powered analysis using external language model APIs - open router -Vite for development and production builds -Lovable for modern responsive UI components to create a seamless user experience across devices

Challenges we ran into

-One of the biggest challenges was integrating multiple services while maintaining a seamless user experience under tight hackathon timelines. We had to coordinate authentication, user profiles, database operations, and AI-powered product analysis while keeping the application fast and intuitive.

-A significant obstacle was finding reliable product data sources. Many available APIs suffered from incomplete ingredient information, limited product coverage, restrictive rate limits, or inconsistent data quality. We evaluated multiple providers, validated responses, and built safeguards to handle missing or unreliable data so that users could still receive meaningful recommendations.

-Another challenge was balancing accuracy with performance. AI-generated insights are only valuable if they are trustworthy and delivered quickly, so we optimized our workflow to reduce latency while maintaining high-quality recommendations. Ensuring smooth communication between the frontend, backend services, and AI models required extensive testing and iteration throughout the development process.

Accomplishments that we're proud of

-Successfully building an end-to-end product analysis platform -Integrating user profiles, and history tracking -Creating personalized recommendation workflows -Designing a clean and intuitive user experience -Deploying a scalable architecture that can support future growth

The application combines user preferences, product data, and AI analysis to generate personalized recommendations in real time.

What we learned

This project taught us valuable lessons about integrating AI into real-world consumer applications. We gained hands-on experience working with modern full-stack technologies, authentication systems, cloud databases, and external APIs. We also learned the importance of validating third-party data sources, handling incomplete information gracefully, and optimizing AI workflows for both speed and accuracy. Most importantly, we learned how to rapidly prototype, test, and iterate under hackathon constraints while maintaining product quality.

What's next for PurePicks

We see PurePicks evolving into a comprehensive consumer decision-making platform. Our next steps include: -Expanding product coverage through additional data sources and partnerships. -Improving recommendation accuracy with more advanced AI models and personalization. -Adding barcode scanning for instant product identification. -Supporting deeper health, dietary, allergen, and sustainability analysis. -Developing native mobile applications for iOS and Android. -Building a community-driven ecosystem where users can share reviews, insights, and product discoveries.