Inspiration
“That Damn… Dish” Theory. It’s a practical scenario that wherever you visit, maybe a special occasion, a restaurant in another city, or even another country there is always a great dish that you tasted and would love to make on your own.
What it does
Recipeon is designed to make that happen effortlessly. The core idea is simple: Take a snap of the dish and let the AI do the rest.
How we built it
- The application follows Clean Architecture principles and established industry best practices to ensure scalability, maintainability, and testability.
- Flutter is used to build the frontend, Node.js powers the backend, and AWS is used for the infrastructure.
Challenges we ran into
- General build–test–fix cycles throughout the development process
- Security: Secured backend endpoints using private, self-signed certificates
- Integrating RevenueCat and designing an intuitive paywall while adhering to official documentation and platform guidelines.
- Navigating the App Store release process, including addressing review feedback, fixing last-minute issues, and resubmitting multiple times for approval.
Accomplishments that we're proud of
- I was able to create a character and build a product around it.
- Taking the idea from Figma to production in under four weeks.
- This is the fastest turnaround I’ve achieved so far.
What we learned
- Setting up a clear scope and boundaries is the only way to achieve things in a timely manner.
- Revenue model success does not just lie in solving the problem; providing a quality experience is also a must.
What's next for Recipeon
- Data backup: iCloud Backup for iOS & GDrive Backup for android
- From ingredients to recipes: get recipe suggestions based on what you have on hand, with region or location-specific options.
- Favorite Recipe System: Allow users to maintain a dedicated list of favorite recipes.
- Watch App for Grocery List: A watch app to track grocery lists, enabling shopping without taking the phone out of the pocket.
- Unified Recipe Database: With proper privacy policy controls, build a database from users’ public searches.
- Custom Serving: Currently, users can adjust serving size during search. This feature focuses on adjusting servings after the search and regenerating enhanced recipe instructions.
Built With
- ai
- amazon-web-services
- dart
- express.js
- flutter
- ios
- llm
- mongodb
- nginx
- node.js
- openai
- pm2
- rest
- security

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