Recoshelf utilizes AI-driven image recognition technology to identify and evaluate grocery products based on their nutritional quality and ingredient safety. The app enables users to quickly exclude unhealthy items and generate personalized shopping lists, addressing the growing consumer demand for healthier eating options amidst rising food allergies and digestive health issues.
Funding
$470K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many consumers struggle to identify healthy grocery products due to misleading labeling, complex ingredient lists, and a lack of readily available nutritional information at the point of purchase. This makes it difficult to avoid unhealthy ingredients, manage allergies, and make informed dietary choices.
Solution
Recoshelf is a mobile application that uses AI-powered image recognition to instantly analyze grocery products on store shelves. By scanning product photos, the app identifies ingredients, evaluates nutritional quality, and flags potential allergens or unhealthy components. Users can customize the app to exclude specific ingredients and generate personalized shopping lists based on their dietary needs and preferences. This enables consumers to quickly identify and select healthier options, promoting better eating habits and simplifying the shopping experience.
Target Audience
Recoshelf targets health-conscious consumers, individuals with food allergies or dietary restrictions, and anyone seeking to make informed and healthier food choices while grocery shopping.
Features
- AI-powered image recognition for instant product analysis
- Identification of unhealthy or allergenic ingredients (e.g., gluten, E-components)
- Nutritional quality scoring based on calories, fat, sugar, and artificial ingredients
- Product comparison feature highlighting nutritional differences
- Customizable ingredient exclusion lists
- Automatic generation of preferred product lists