A full-stack withdrawal management system built with Spring Boot and Angular.

• Backend: Java 17, Spring Boot 4.1.0, H2 Database, JPA/Hibernate
• Frontend: Angular, TypeScript, CSS

1. Clone the repository
2. Open in IntelliJ IDEA
3. Run Enviro365Application.java
4. Backend runs on http://localhost:8080
5. H2 Console available at http://localhost:8080/h2-console
   • JDBC URL: jdbc:h2:mem:enviro365db
   • Username: SA
   • Password: (leave empty)

1. Navigate to enviro365-frontend folder
2. Run npm install
3. Run ng serve
4. Frontend runs on http://localhost:4200

• Retirement withdrawals only allowed for investors older than 65
• Withdrawal amount cannot exceed the product balance
• Withdrawal amount cannot exceed 90% of the product balance

• Global exception handling with clean JSON error responses
• DTO layer to control data exposure
• Input validation with meaningful error messages
• UI validation preventing zero or negative withdrawal amounts

• AI was primarily used to assist with planning the project's architecture and defining the overall structure of the application. Rather than providing direct solutions, it served as a mentor and learning guide, helping to identify potential approaches and encouraging critical thinking throughout the development process.
• Whenever challenges arose, AI was used to explore possible solutions and provide guidance, but only after a genuine attempt had been made to solve the problem independently. The goal was not to rely on AI as a code generator, but to use it as a tool for learning, problem-solving, and gaining a deeper understanding of software development concepts.
• All architectural decisions were carefully considered and reasoned through, with AI providing guidance rather than making decisions on my behalf. As a result, I fully understand and can confidently explain every component, feature, and piece of code within this project.