Instinct —
The Future of Predictive Retail

Product Designer / UX Designer

Proactive Discovery

INSTINCT reframes fashion e-commerce from reactive search to proactive discovery—where the system understands intent before the user expresses it.

Core Experiences

Context

Traditional e-commerce relies on search, creating friction and decision fatigue.

Problem

Users must actively search, slowing discovery and reducing engagement.

Insight

Product discovery is time-consuming and repetitive, leading to friction, decision fatigue, and ultimately user drop-off.

Predictive Homepage

Uses real-time context to deliver tailored styling, with AI-curated outfits that anticipate needs like weather and location, guiding users from discovery to a complete wardrobe.

Capsule

The capsule screen uses AI to curate cohesive, context-aware pieces, with a versatility score that shifts shopping from individual items to a connected system.

Predictive Insights Layers

Shifts AI from assistant to stylist—surfacing context, evolving preferences, and wardrobe gaps.

Predictive Impact

How predictive styling improves discovery and purchase behavior.

20%

Conversation Rate

Faster Purchase

30%

Average Order Value

Outfit pairings increase basket size

40%

Product Discovery

Curated edits surface deeper
inventory

-30%

Faster Engagement

Users engage with products
immediately

Approach

I approached INSTINCT as a system-level design, not a feature—stepping back to rethink how fashion e-commerce works at its core.

I began by identifying friction in the experience: users rely on search without clear intent, leading to slow, fragmented decisions. This revealed an opportunity not to improve search, but to remove the need for it.

I reframed the model into a predictive, context-aware platform, using signals like behavior, time, and environment to introduce direction earlier—shifting from reactive browsing to proactive discovery.

Rather than designing isolated screens, I built a connected system: Product → Story → Experience → Purchase

A key focus was making AI transparent and trustworthy, introducing a reasoning layer (“Why an I seeing this?”) to explain recommendations and guide decisions.

The result is a more focused, intuitive experience—reducing choice overload and creating a faster, more confident path to purchase.

Styled Together

Complete, AI-curated outfits designed for real-life moments—combining individual pieces into a cohesive look. It provides contextual styling insight, a confidence score, and seamless actions to purchase the full outfit or explore items individually, shifting shopping from single products to fully styled decisions.

Product Detail

This screen recommends the right size, explains why the item fits the user’s style and context, and highlights compatibility with their existing wardrobe. By adding insights like match scores, pricing context, and outfit completion, it shifts the experience from product browsing to informed, personalized selection.