NEURA Kicks - AI-Powered Sneaker Shopping App

NEURA Kicks is an AI-powered sneaker shopping app designed to personalize product discovery, reduce decision fatigue, and help users find the right sneaker faster.

Most sneaker apps rely heavily on manual browsing. Users scroll through hundreds of products before finding something relevant, and when discovery becomes exhausting, they abandon the journey before purchasing. I wanted to explore how AI could solve that: not as a gimmick, but as the core of the experience.

This is a self-initiated concept project. I identified the problem, defined the product scope, and designed the full experience independently, from research through interactive prototype.

Through competitor analysis of leading sneaker shopping apps, I found the same issues repeated:

Generic recommendations that don't adapt to user preferences

Cluttered product grids with too much visual noise

Weak filtering systems that don't narrow results meaningfully

Long decision-making time with no guidance for undecided shoppers

The core question: how might we help users discover the right sneaker with less effort and more confidence?

When product discovery is exhausting, conversion drops. The problem isn't selection. It's that nobody's helping the user navigate it.

Research & Analysis I reviewed leading sneaker shopping apps, analyzed their onboarding and discovery flows, and identified friction points in search, filtering, and recommendation systems.

Information Architecture I mapped the primary user journey: Open App → Discover Recommendations → Explore Product → Save / Purchase. Every screen exists to move the user forward through that sequence.

Wireframing Low-fidelity wireframes tested navigation structure, AI recommendation placement, product card hierarchy, and checkout flow simplicity.

Design Refinement An important pivot happened early. Initial explorations showed too many products on the home screen, which created the same visual clutter I was trying to solve. I reduced the number of visible products and prioritized fewer, higher-quality recommendations supported by AI cues.

AI-First Home Screen Instead of leading with categories, I placed personalized recommendations at the top. This reduces cognitive load and immediately gives users relevant options rather than asking them to browse.

Focused Product Cards Each card highlights the sneaker image, model name, price, and an AI match indicator. The goal was to help users evaluate products at a glance without opening every detail page.

Premium Visual Language Dark surfaces with high-contrast accents create a premium retail feel. Clean, modern typography improves readability. Generous whitespace keeps the interface breathable despite rich imagery.

Micro-Interactions The wishlist interaction was a detail I was particularly proud of: the save action provides immediate visual feedback without interrupting the browsing flow.

Trade-offs I intentionally reduced the amount of information on product cards. Users see fewer details initially, but the cleaner presentation makes scanning significantly faster. Less on screen, more confidence per decision.

UI Design: Figma

User Flows & Planning: FigJam

Design System: Figma components and tokens

Prototyping: Interactive prototype in Figma

This project focused on product strategy, UX, UI design, and prototyping. It was not developed into a production application. I worked independently across every phase.

The final deliverable is a complete concept piece: high-fidelity mobile app screens covering the core shopping experience, a reusable design system with components and tokens, and an interactive prototype demonstrating the end-to-end flow from discovery to purchase.

AI features should reduce effort, not add complexity

Personalization works best when surfaced immediately, not buried in settings

E-commerce conversion is strongly influenced by clarity and confidence

Less information on screen can improve decision speed

What I'd do differently: If I continued this project, I'd conduct usability testing with sneaker buyers to validate the recommendation flow and refine the AI explanation system.