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StyleDebt

Student Capstone Project 路 Spring 2026

StyleDebt Match More, Spend Less

An AI-powered personal shopper that begins with what students already own鈥攈elping every new purchase fit their closet, budget, and real life.

Student Team Lucy Filippini and Amanda Sargent
Faculty Advisor Prof. Kirthi Kalyanam
Program Generative AI in Retail
Cohort Spring 2026
Amanda Sargent and Lucy Filippini presenting the StyleDebt capstone project

The Student Problem

Students buy first and match later.

Shopping as a student is always a balance. You want to look good for class, interviews, presentations, and events鈥攂ut every purchase has to fit a budget and work with what you already own.

The result: closet clutter, underused clothes, and a high cost per wear.

Before StyleDebt

Buy first. Match later.

Impulsive purchases, closet clutter, and pieces that rarely get worn.

After StyleDebt

Know your closet before you spend.

Smarter matches, fewer regrets, and a lower cost per wear.

Meet StyleDebt

Turn your closet into intelligence

StyleDebt is an AI-powered closet management and personal-shopping app. It tracks what users own, how often they wear each item, what each item really costs over time, and what gaps actually exist in their wardrobe.

The prototype applies lessons from Instacart Smart Shop and Ulta Shade Finder: AI works best when it removes friction and narrows choices to options that make sense for the individual customer.

Instead of asking only, 鈥淲hat should I buy next?鈥 StyleDebt asks, 鈥淲hat do I already own, and what would actually add value?鈥
  1. 1 Build your closet Add clothes manually or parse purchase receipts with AI.
  2. 2 Track real use Log what gets worn and build a clearer picture of the wardrobe.
  3. 3 Calculate value Measure cost per wear and identify the most and least useful items.
  4. 4 Shop smarter Recommend versatile purchases that fill genuine wardrobe gaps.

Cost-Per-Wear Insight

One smarter purchase can change the math.

$100 jacket

Worn 50 times

$2 per wear

$40 top

Worn once

$40 per wear
It is not about how much you spend. It is about how smart you wear.

Daily Outfit Updates With OpenClaw

Make closet tracking almost effortless

The hardest part of a closet app is keeping it current. OpenClaw can send a simple daily reminder to photograph an outfit, recognize what was worn, and update StyleDebt automatically.

That creates better cost-per-wear calculations, clearer wardrobe-gap insights, and recommendations grounded in actual behavior.

9:00 AM 路 Outfit reminder Don鈥檛 forget to capture today鈥檚 outfit before you head out.
9:01 AM 路 Outfit captured Today鈥檚 look has been added to your closet history.
9:02 AM 路 Insights updated Your cost-per-wear and wardrobe recommendations just improved.

Future Scope

From prototype to possibility

  • Forward receipts to receipts@styledebt.app for automatic parsing.
  • Track monthly spending, category patterns, and cost-per-wear trends.
  • Share outfits and wardrobe insights socially.
  • Connect with retailers such as Nordstrom, Zara, and Anthropologie.
  • Support resale, sustainability, and smarter wardrobe decisions.

With Gratitude

Thank you, Professor Kirthi Kalyanam

We are grateful to Professor Kirthi Kalyanam and the Retail Management Institute at SA国际传媒 for creating the Generative AI in Retail workshop and giving us the opportunity to build, test, and present StyleDebt.

The project helped us look beyond product recommendations and ask a more useful question: how can AI help shoppers make better decisions in the context of their real lives?

A student capstone from the Generative AI in Retail workshop at the Retail Management Institute, SA国际传媒.