BudgetU

BudgetU is a student-first financial literacy platform that uses AI to analyze spending habits, optimize budgets, and teach real-world money skills in a simple, stress-free way.

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BudgetU — Project Story

Inspiration

I built BudgetU after realizing something simple but uncomfortable: as a college student, I was spending far more than I should.

Between eating out, subscriptions, and random purchases, my spending did not match my long-term goals. Around the same time, I started learning about investing and retirement accounts through my finance coursework at Georgetown and my internships. I realized most students around me were in the same position. We were motivated and ambitious, but we were not taught how to manage money early.

I wanted to build something for students like me. A tool that helps you understand where your money goes, how to budget realistically, and how early investing compounds over time.

Because the math is simple:

[ FV = PV \cdot (1 + r)^t ]

Saving \$200 per month starting at 18 instead of 28 creates a massive difference in future value. Seeing that visually makes the lesson real.

BudgetU started as a way to fix my own habits and became a project to help other students build financial discipline earlier.

How I Built It

This project was also a chance to push my technical skills beyond coursework into real deployment.

I built BudgetU as a full stack web application using:

  • Frontend: Web interface for budgeting inputs and dashboards
  • Backend: AWS Lambda for serverless logic
  • Deployment: AWS Amplify for hosting and CI/CD
  • Database: Cloud storage for user data
  • AI Tools: Cursor Pro, ChatGPT, Gemini, and Claude Code to accelerate development

Using AWS was a big step for me. I learned how to:

  • Deploy a full stack app with Amplify
  • Write serverless functions with Lambda
  • Handle authentication and data flow
  • Debug cloud systems instead of only local code

As someone who usually builds Python scripts, C++ assignments, or analytics tools for internships at AWS and my work with automation projects, building something that actually runs online for users was a huge learning experience.

What I Learned

This project taught me far more than just coding.

Technical Lessons

  • How frontend and backend systems communicate
  • How to structure APIs and serverless functions
  • How deployment pipelines actually work
  • How to debug production issues
  • How to use AI coding tools responsibly

Personal Lessons

  • Most students do not track spending at all
  • Small financial habits matter more than big ones
  • Teaching something forces deeper understanding
  • Building real projects is the best way to learn

As a Georgetown student studying Finance and Operations & Analytics with a CS minor, this project connected everything I am learning into one system that actually helps people.

Challenges I Faced

AWS Learning Curve

AWS documentation can be overwhelming. Figuring out Amplify and Lambda configuration took a lot of trial and error.

Common issues I ran into included:

  • Broken deployments
  • Environment variable bugs
  • Authentication issues
  • Lambda timeout errors

Designing for Students

Making a budgeting tool that students actually want to use is harder than writing code.

Students do not want complicated dashboards, finance jargon, or too many inputs. I had to simplify everything while still keeping it useful.

Data Modeling

Deciding how to store spending categories, budgets, and goals required more thought than expected.

I had to think about equations like:

[ \text{Savings} = \text{Income} - \text{Expenses} ]

But also practical questions:

  • What counts as fixed versus variable spending?
  • How should subscriptions be tracked?
  • How do you handle irregular income?

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