Designing a first-in-class user experience of an AI-powered e-commerce distribution operations optimization platform
Context
This AI‑powered e‑commerce distribution operations optimization platform helps Amazon FBA sellers optimize spend, content, and operations. When I joined, the product had PMF signals and an active user base, but needed a scalable design system, a coherent information architecture, and a higher‑fidelity UX to sustain growth and broaden adoption.
Objective
Evolve the MVP into a production platform that:
exposes AI insights continuously and transparently
clarifies what the AI decided vs what the user decided
gives expert users granular control without overwhelming newcomers
compresses time‑to‑insight with dense but readable visualizations
Role & Collaboration
I led design across IA, systems, and data visualization while partnering daily with product and engineering. I conducted and synthesized user interviews and feedback sessions, and collaborated with leadership and operations to align on business goals and terminology.
Approach
Systematize and scale
Built a shared design language: tokens, components, patterns, and documentation for consistent handoff and faster iteration.
Reworked the IA to reflect how sellers think: catalog (ASIN/SKU), spend, content quality, forecasting, and AI recommendations.
Make AI legible and trustworthy
AI is present on every critical screen with inline guidance, next‑best actions, and rationale.
Clear attribution of actions and outcomes: what the model changed, what the user changed, and the impact of each.
Compress time‑to‑insight with purposeful density
Designed dashboards that surface the “first 30‑seconds aha”: status, outliers, and trends before deep‑dive.
Used sparklines, segmented timelines, stacked bars, and guided tables to show state, change, and causality without clutter.
Signature Modules
AI Content Builder (early 2023): A from‑scratch experience for generating and auditing product content when few credible references existed. Balanced AI suggestions with human controls, versioning, and publish‑readiness checks.
PPC Budget Strategy Visualization: A timeline view of strategy by week, with planned vs actual performance and a forward forecast to inform next‑week allocation. Connects spend to outcomes, not just spend to spend.
Product Content Quality & Publishing Dashboards: Grouped by ASIN/SKU, with diagnostics for missing or weak content, items not pushed to Amazon, and prioritized fixes. Converts a messy catalog into an actionable queue.
Research & Validation
Conducted iterative user interviews and feedback cycles with power users and operators.
Used testable prototypes to tune thresholds, visual density, and copy until users reliably reached the intended insight path.
Outcomes (Qualitative)
Faster decision loops due to always‑visible AI guidance and clearer provenance of changes.
Higher confidence in AI recommendations thanks to transparent rationale and human override paths.
Team velocity gains from the design system and cleaned‑up IA, reducing rework and enabling parallel delivery.
Impact
The platform’s UX evolved from PMF‑level to a differentiated, production‑grade experience suited to expert decision‑making. The platform’s clarity and control became a selling point with customers, while the design system and IA reduced friction for continuous feature growth.