2 weeks
Architecture Design
2 weeks were spent designing a scalable and robust architecture for the application, ensuring it could handle a large number of users and integrate seamlessly with the client’s existing systems.
Summary
This project involved developing an AI-powered application that generates personalized recipes based on the ingredients available in users' kitchens. The client, a tech-savvy food startup, needed an intelligent, automated system to provide users with creative and tailored recipes, reduce food waste, and enhance cooking experiences. The main problem was users frequently having leftover ingredients and not knowing how to use them effectively, leading to food wastage and limited meal variety.
Business Impact
Reduced Food Waste: The AI-powered application helped users utilize leftover ingredients effectively, reducing food wastage.
Improved User Satisfaction: Personalized and creative recipes based on available ingredients led to higher user satisfaction and engagement.
Increased Application Usage: The innovative and useful features of the application attracted more users, boosting overall application usage.
Scalability: The scalable architecture ensured the application could handle peak usage periods without performance degradation.
Enhanced Brand Reputation: The client’s commitment to reducing food waste and providing value to users through innovative technology enhanced their brand reputation.
Tech challenges
The primary technical challenge was developing a highly accurate NLP model capable of understanding diverse ingredient inputs and generating relevant recipes. Additionally, integrating the application with existing recipe databases and ensuring data accuracy posed significant technical difficulties. Ensuring data security and privacy while handling user preferences and nutritional information was also a critical challenge.
Timelines
1
2
3 weeks
Data Integration
Over 3 weeks, various data sources were integrated, including recipe databases, nutritional information, and user preferences, to provide the AI with comprehensive data for generating accurate and personalized recipes.
3
4 weeks
Backend Development
4 weeks were spent developing the backend, focusing on the application’s core functionalities, such as natural language processing (NLP) and machine learning algorithms.
4
2 weeks
UI Design
The UI design phase took 2 weeks, during which an intuitive and user-friendly interface for the application was created.
5
1 week
Integration
Integrating the application with the client’s existing platforms and databases took 1 week.
Case Study