Foodlabs

Implemented an application with a recommendation algorithm for personalized food selection, significantly increasing user engagement – HealthTech

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Project amount

+50 000 $

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Timeline

6 months

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Team

12 persons

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Stack

Flutter, Nodejs, Python, React, PostgreSQL

About the project

Foodlabs is a mobile app that develops healthy eating plans based on health screening results. By integrating health data and the expertise of nutritionists, it offers personalized nutritional recommendations that promote optimal well-being and meet individual health needs.

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Main request

The primary request from the client was to develop a mobile application that could offer personalized food recommendations based on users' medical parameters, dietary preferences, and health goals. The goal was to create an intuitive and user-friendly platform that would provide tailored food suggestions to help users maintain a healthy lifestyle.

Problem solving

To address the client's need for personalized food recommendations, we developed mobile applications for both iOS and Android using Flutter, ensuring a consistent and seamless experience across platforms. Flutter allowed us to build a high-performance app with a single codebase, reducing development time and simplifying future updates.

We collaborated closely with the company's methodologists to design and implement a recommendation algorithm. This algorithm analyzes user data to offer personalized food selections from the company's partner network, ensuring that recommendations align with users' preferences and dietary needs.

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Result

The app now provides the following key functionalities:

Personalized product recommendations: Based on user preferences, behavior, and analytics, the algorithm suggests products from the company's partners.

Cross-platform consistency: Both iOS and Android apps deliver the same high-quality user experience, optimized for performance and responsiveness.

User engagement tools: Integrated features like user feedback on recommendations, allowing the algorithm to continuously improve and refine its suggestions.

This solution resulted in a significant increase in user engagement by delivering relevant, data-driven recommendations that keep users connected to the platform and its partner services.