Resume Matcher
An AI-assisted product that helps college students compare their resumes against internship job descriptions.
- Defined the problem, target user, MVP, requirements, acceptance criteria, and success metrics in a full PRD.
- Tested the MVP with different job descriptions and checked whether the AI stayed grounded in the resume and job description without fabricating experience.
- Compared Version 2 ideas using RICE and prioritized Job Fit Score after it received the highest score.
- Added and tested Job Fit Score and Dark Mode as the next iteration.
Subscription Tracker
A product designed to help college students understand and manage recurring subscription spending.
- Shaped the product around a clear student pain point: losing track of recurring costs and renewal dates.
- Built and validated the first MVP around subscription entry, spending visibility, and renewal tracking.
- Evaluated two next-step features with RICE and MoSCoW, then chose Edit Subscription based on overall product value, not just the highest score.
- Iterated the product by adding editing functionality and testing whether subscription details updated correctly.
Subscription Tracker Competitive Analysis
Analyzed Rocket Money, Quicken, and PocketGuard to understand how existing subscription-management products serve users and identify opportunities for a simpler student-focused experience.
- Compared competitors across pricing, onboarding, bank linking, subscription tracking, renewal visibility, budgeting, cancellation support, and ease of use.
- Identified tradeoffs between automation, complexity, privacy, and simplicity.
- Identified a student-focused market gap and created product recommendations based on the research.
VT Meal Plan Selector
A web-based recommendation tool that helps Virginia Tech students choose a dining plan based on their dining preferences and expected spending.
- Designed the recommendation flow around two inputs: dining preferences and estimated daily spending, using them to compare available Virginia Tech meal plans.
- Built the recommendation logic in Java and implemented the web interface using HTML, CSS, and JavaScript.
- Defined acceptance criteria and tested both recommendation paths, including a multiple-dining scenario where $20 in estimated daily spending produced a Maroon Plus recommendation.