Dress Me Up

Introduction & Background

In early 2020, we kicked off “Dress Me Up” as part of our practical business degree—a 16-week sprint to bring AI personalisation to fashion shopping. Our dream was simple: give people a stress-free way to discover outfits that actually reflect their style, and let them chat directly with tailors or designers to tweak every detail.

Problem Statement

We all know the feeling: you scroll through page after page of clothes online, get decision fatigue, and still end up settling for something “good enough.” Traditional sites relied on broad filters or generic “if you like X, you’ll love Y” recommendations—and it just wasn’t cutting it. There was no platform offering genuinely tailored style advice plus an easy route to custom fittings.

Objectives & Goals

Right from day one, we set out to:

1. Prototype a seamless flow from onboarding into AI-powered outfit suggestions.

2. Simulate smart recommendations so users feel understood, even without a full AI engine behind the scenes.

3. Build a buyer-to-maker marketplace where you could browse looks and ping a designer or tailor for adjustments.

Challenges

We faced a ticking clock—16 weeks don’t stretch far when you’re balancing classes and client-style deliverables. On the tech side, we needed to fake the AI magic convincingly, without a backend data pipeline. And on the UX side, it was tricky to design for two very different users—shoppers craving inspiration, and tailors wanting clear-cut custom orders—all within the same app.

Market Research

Instead of hypothetical classmates, we went straight to the market:

• 100 survey responses from fashion-enthusiastic adults, probing how they shop online and what frustrates them most.

• Eight in-depth interviews with local independent tailors and freelance designers, where we dug into how they handle custom requests today—and what tools they wished they had.

• Competitive audit of five top apps, mapping out where everyone else fell short on true personalisation and custom-order flows.

Findings

What jumped out was clear:

• 78% of respondents said they’d pay extra for spot-on style suggestions that feel “made for me.”

• All tailors we spoke to wanted a simple intake form plus a chat window that didn’t require email back-and-forth.

• Shoppers would bail out after three clicks if they still hadn’t landed on something that spoke to them.

User Needs & Pain Points

From that research, we distilled two big themes:

• Needs: Fast, confidence-boosting outfit recommendations; a fun, quiz-style onboarding; and an in-app chat for bespoke tweaks.

• Pain Points: Endless scrolls of irrelevant products; clunky, multi-step custom-order processes; and checkout flows that felt like homework.

Unique Features

To address those, our prototype showcased:

• A 60-second style quiz with visuals and sliders so users could express their taste in under a minute.

• A rule-based “look board” demo illustrating how the app could narrow hundreds of items into a curated selection.

• An in-app designer chat where you could say “swap the blue lining for red,” share measurements, and get instant mock-up previews.

Conclusions & Reflections

While Dress Me Up never went live, the prototype taught us so much. Users loved the mix of speedy recommendations and direct designer access, and tailors appreciated having a single, clear channel for custom orders. For me personally, this project was a powerful reminder that real-world research plus lean prototyping exposes hidden UX and technical hurdles long before you write a line of production code—and that a human-centered narrative makes even “mock” AI feel magically real.