AI-powered commerce

As Design Architect for selling experiences, I drove AI innovation across eBay’s ecosystem, connecting emerging opportunities across hackathon concepts, camera-first explorations, and platform integrations. I aligned product and experience decisions across these initiatives, helping them inform and build on one another.

Advances in multimodal AI and on-device intelligence are shifting digital services toward more contextual, system-level experiences. For eBay, this presented an opportunity to extend shopping and selling into real-world moments. Apple Intelligence offered a way to bring visual discovery and contextual interactions into the eBay experience, while raising new challenges around understanding user intent, working within platform constraints, and maintaining user control.

Apple Intelligence enables people to interact with their surroundings and access relevant actions without opening a specific app. Visual Intelligence was particularly compelling for eBay: using camera and on-screen context, people could identify objects, explore information, and initiate commerce actions directly from the OS.

The core design challenge was understanding what people wanted to accomplish and routing them to the right experience. Rather than immediately categorizing someone as a buyer or seller, I designed around their underlying Jobs-to-be-Done—such as assessing an item’s market value.

I designed a dedicated intermediary page to bridge the initial visual search in Apple Intelligence with relevant eBay app experiences, providing a contextual entry point to different marketplace actions and workflows.

I led product strategy for an AI-powered product research concept from eBay’s 2024 hackathon, turning real-world photos into actionable market intelligence to eliminate seller sourcing friction.

Sellers often waste time and capital because they lack instant visibility into real-time demand and resale margins before investing effort into listing. To solve this, the experience combined computer vision with eBay’s marketplace data—allowing sellers to snap a photo of an outfit, garage sale haul, or product stack. AI isolates individual items, surfaces pricing trends, and flags high-margin inventory to guide competitive pricing.

Beyond streamlining research, the concept connected visual discovery directly to multi-item listing workflows, unlocking new avenues for curated outfit creation and social-led sourcing inspiration. Executive leadership selected the concept for production development to bridge visual discovery with smarter sourcing.

Building on this exploration, I contributed to a broader cross-functional sprint focused on eBay’s camera-first and visual commerce strategy. We explored how camera experiences could become a gateway to more than search—connecting people through shared interests, inspiration, storytelling, and recommendations.

Scenarios included sharing discoveries and getting advice while sourcing at a flea market, creating inspiration boards, visualizing products in personal spaces, and turning community inspiration into shoppable collections. The opportunity was to make community and human connection drivers of commerce, with technology adding value through visual matching, product identification, pricing insights, AR visualization, and contextual AI assistance.

I drove the experience architecture by mapping how camera capabilities, the AI assistant, and user intents integrate across the app. This involved defining clear boundaries between in-camera actions and dedicated app destinations, enabling seamless, context-preserving transitions between buying, selling, and live streaming.