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[Case 01]

Transforming Product Discovery - 60% Less Search Time

E-commerce

60% Less Search Time Through Simplified AI Chat Experience

Boosting Conversion Rates for E-commerce Discovery

[Project Overview]

Faced with fragmented stores catalogs and slow product search, We designed UONEX's AI conversational interface. By enabling natural language discovery and personalized recommendations, we reduced search time by 60% and increased engagement by 3x.

[Problem Statement]

Traditional Store shopping in India faces a critical discovery challenge: shoppers waste 2-3 hours visiting multiple stores across sprawling malls, often leaving frustrated without finding what they want. Meanwhile, premium fashion brands struggle to connect their inventory with ready-to-buy customers already at the mall.

[Industry]

E-commerce

[My Role]

Founding Designer

[Platforms]

iOS & Android

[Timeline]

Nov 2025- Jan 2026

Download the App

Download the App

[Persona]

Priya Sharma

College Student

Find exactly what she needs without wasting time browsing multiple stores or apps.

Age: 22

Location: Mumbai, India

Tech Proficiency: Moderate

Gender: Female

[Goal]

Find trendy outfits quickly across multiple brands

Get outfit inspiration for upcoming events

Stay within budget (₹2000-5000 per item)

[Frustrations]

Wastes entire Saturdays visiting 10+ stores

Can't find Instagram-inspired looks in physical stores

Misses limited edition items because doesn't know which store has them

[Process]

[01] User Research (Discovery & Validations)

Interviewed 15 users to identify core pain points and preferences, synthesizing findings into a research framework that guided feature prioritization and UX improvements.

Mapped user journeys based on behavioral insights, structured information architecture to surface relevant data at key touchpoints, and connected research findings directly to user needs.

Conducted usability testing with rapid wireframes across multiple scenarios to understand user behaviors and validate design assumptions before high-fidelity mockups.

[01] User Research (Discovery & Validations)

Interviewed 15 users to identify core pain points and preferences, synthesizing findings into a research framework that guided feature prioritization and UX improvements.

Mapped user journeys based on behavioral insights, structured information architecture to surface relevant data at key touchpoints, and connected research findings directly to user needs.

Conducted usability testing with rapid wireframes across multiple scenarios to understand user behaviors and validate design assumptions before high-fidelity mockups.

[01] User Research (Discovery & Validations)

Interviewed 15 users to identify core pain points and preferences, synthesizing findings into a research framework that guided feature prioritization and UX improvements.

Mapped user journeys based on behavioral insights, structured information architecture to surface relevant data at key touchpoints, and connected research findings directly to user needs.

Conducted usability testing with rapid wireframes across multiple scenarios to understand user behaviors and validate design assumptions before high-fidelity mockups.

[02] Insights

Users don't just want product search - they want conversational guidance like talking to a knowledgeable friend.

Bridge both Physical & Digital Experience - don't replace in-store experience, enhance discovery before arrival because Gen Z values tactile experience but wants efficiency

Conversational UI needs visual reinforcement.

[02] Insights

Users don't just want product search - they want conversational guidance like talking to a knowledgeable friend.

Bridge both Physical & Digital Experience - don't replace in-store experience, enhance discovery before arrival because Gen Z values tactile experience but wants efficiency

Conversational UI needs visual reinforcement.

[02] Insights

Users don't just want product search - they want conversational guidance like talking to a knowledgeable friend.

Bridge both Physical & Digital Experience - don't replace in-store experience, enhance discovery before arrival because Gen Z values tactile experience but wants efficiency

Conversational UI needs visual reinforcement.

[03 Design Solution]

Designed a conversational AI interface that replaces traditional search filters with natural language queries, enabling users to discover products through simple chat interactions.

Implemented contextual product recommendations based on user preferences, budget, and shopping intent, reducing decision time while maintaining personalization.

Created a unified product catalog integration that aggregates inventory across multiple brands stores, eliminating the need to switch between different store apps.

[03 Design Solution]

Designed a conversational AI interface that replaces traditional search filters with natural language queries, enabling users to discover products through simple chat interactions.

Implemented contextual product recommendations based on user preferences, budget, and shopping intent, reducing decision time while maintaining personalization.

Created a unified product catalog integration that aggregates inventory across multiple brands stores, eliminating the need to switch between different store apps.

[03 Design Solution]

Designed a conversational AI interface that replaces traditional search filters with natural language queries, enabling users to discover products through simple chat interactions.

Implemented contextual product recommendations based on user preferences, budget, and shopping intent, reducing decision time while maintaining personalization.

Created a unified product catalog integration that aggregates inventory across multiple brands stores, eliminating the need to switch between different store apps.

[04] Testing & Iteration

Conducted A/B testing with 500 users, comparing the Traditional and redesigned flows.

Gathered feedback through prototype testing and refined the chat interface based on user preferences for conversation tone, recommendation depth, and visual product displays.

Iterated on mobile-first layouts with larger touch targets for product cards, optimized chat bubbles for readability, and streamlined the conversation-to-add to list flow.

[04] Testing & Iteration

Conducted A/B testing with 500 users, comparing the Traditional and redesigned flows.

Gathered feedback through prototype testing and refined the chat interface based on user preferences for conversation tone, recommendation depth, and visual product displays.

Iterated on mobile-first layouts with larger touch targets for product cards, optimized chat bubbles for readability, and streamlined the conversation-to-add to list flow.

[04] Testing & Iteration

Conducted A/B testing with 500 users, comparing the Traditional and redesigned flows.

Gathered feedback through prototype testing and refined the chat interface based on user preferences for conversation tone, recommendation depth, and visual product displays.

Iterated on mobile-first layouts with larger touch targets for product cards, optimized chat bubbles for readability, and streamlined the conversation-to-add to list flow.

[Outcome]

60% reduction in product discovery time compared to traditional E-commerce app browsing.
3x increase in user engagement and session duration through conversational shopping interface.
70% of users reported higher satisfaction with personalized recommendations, as measured by post-interview feedback.

[Key Learnings]

Conversation design is critical

Users expect natural, context-aware dialogue, generic AI responses break trust and engagement immediately.

Conversation design is critical

Users expect natural, context-aware dialogue, generic AI responses break trust and engagement immediately.

Conversation design is critical

Users expect natural, context-aware dialogue, generic AI responses break trust and engagement immediately.

Progressive disclosure works best

Testing revealed users prefer guided conversations over open-ended queries, with AI asking clarifying questions to narrow choices.

Progressive disclosure works best

Testing revealed users prefer guided conversations over open-ended queries, with AI asking clarifying questions to narrow choices.

Progressive disclosure works best

Testing revealed users prefer guided conversations over open-ended queries, with AI asking clarifying questions to narrow choices.

Visual + conversational hybrid wins

Combining chat interface with visual product cards increased comprehension and reduced decision fatigue significantly.

Visual + conversational hybrid wins

Combining chat interface with visual product cards increased comprehension and reduced decision fatigue significantly.

Visual + conversational hybrid wins

Combining chat interface with visual product cards increased comprehension and reduced decision fatigue significantly.

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