DecisionLab | AI-Powered Product Comparison

Information overload makes online shopping exhausting. Consumers spend hours comparing specs, prices, and conflicting reviews across dozens of tabs, often leading to decision fatigue and buyer's remorse. Standard filters like "price" or "average rating" are rarely enough to capture nuanced, real-world needs like "I need a lightweight laptop for college that can also handle light gaming." DecisionLab solves this by acting as your personal AI shopping assistant. Our platform uses an intuitive three-step process to simplify complex buying decisions. First, users search for and select the products they want to compare, while our system fetches real-time data and pricing. Next, our conversational AI conducts an interactive interview to discover the user's hidden preferences, budget constraints, and specific use cases, dynamically determining what features matter most. Finally, the AI synthesizes this personal context with the product data to deliver a definitive recommendation, complete with a customized scoring breakdown and clear trade-offs. DecisionLab was primarily built primarily using NativelyAI, the AI coding platform that accelerates product development from idea to working app. I used the full token budget to generate core features, structure, and functionality. Once the token budget was fully used, I completed the remaining minor tasks and polish some parts manually. Built with React and powered by Supabase Edge Functions, DecisionLab takes the stress out of shopping by turning hours of tedious research into a simple conversation.