Food waste is a growing concern in the United States, costing the average American approximately $1,800 every year. This cost is a direct result of poor planning and low confidence in home kitchens. Ripe fights food waste by using AI to help users explore new recipes, plan their meals, and gain confidence in the kitchen. Ripe has five main modules -- dashboard, schedule, pantry, groceries, and cooking -- all available on mobile devices. The cooking module uniquely integrates with Alexa devices, including both sound-only devices like the Echo and visual devices like the Alexa Kitchen, to guide the user in real-time through the cooking experience.
As the Project Lead for my group, I guided our team through LEAN UX techniques. Rather than spending 50% of our time resarching, 30% designing, and 20% testing, we created product hypothesis models early. We then ideated and user tested continuously and iteratively using rapid prototyping techniques to find the idea solution for our users as quickly as possible. Our user testing consisted of multiple experiments that resulted in measurable, statistically-significant outcomes: after cooking with Ripe, our users were more confident, happier, and calmer than those who cooked without, and they reported a higher understanding of the ingredients they cooked with.
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While you learn to cook from Ripe, Ripe also learns from you. Its AI and machine learning algorithm do all of the heavy lifting to find recipes and curate custom shopping lists for you. Ripe uses a detailed onboarding process and constant user interaction and feedback to feed its algorithm, resulting in an ultra-specific and highly customized recipe suggestion algorithm. Ripe captures activities users already do each day -- searching for recipes, saving ideas, planning meals, and shopping for ingredients -- and leverages their data to make the meal planning process easier and to reduce the user’s food waste.