Case Study · Product Design · 2024 – 2025

POMU

How I designed AI-powered manufacturer discovery from zero — pairing a dual-input search interface with a scalable design system to cut production time by 40% and drive 350+ followers from launch day.

Role Founding Product Designer & Brand Lead Duration Apr 2024 – Apr 2025 Team 2 Designers, 3 Engineers
Product Design 0 → 1 AI / ML Design System Brand Identity WCAG 2.1 AA B2B SaaS
POMU Platform Overview
Fig. 1 · POMU Platform — AI-powered manufacturer matching for fashion entrepreneurs. Dual image and keyword search built on a CNN + NLP pipeline.
40%
Reduction in new surface production time via the design system
350+
LinkedIn followers from zero in three months post-launch
12+
Discovery interviews with early-stage fashion founders
0→1
Full product surface designed, from research through ship
I · The Challenge

Manufacturer Discovery Is Broken for Independent Founders.

Fashion entrepreneurs searching for manufacturing partners navigate a landscape that hasn't changed in decades. Alibaba listings with no quality signals. Reddit threads of anecdotes. Spreadsheets traded in private Slack groups. Every workaround reveals the same gap: there is no platform built for the way small founders actually discover suppliers.

Pomu set out to close that gap — an AI-assisted B2B marketplace where founders could describe what they wanted to make, upload a reference image, and receive matched manufacturers ranked by fit, capacity, and verified credentials.

  • No quality signals: existing directories gave no transparency on capabilities, certifications, or past work
  • Vocabulary barrier: founders knew their product; they didn't know manufacturing terminology
  • Trust deficit: overseas suppliers with no reviews or accountability structures
  • Platform fragmentation: discovery lived across Reddit, trade shows, referrals — no single surface

My Scope

As the founding product designer, I owned the full design surface with no existing patterns to inherit.

  • Led user discovery — 12+ interviews, affinity mapping, insight synthesis
  • Designed end-to-end flows — onboarding, dual-input search, results, supplier profiles, messaging
  • Built the design system from scratch in Figma — components, tokens, WCAG accessibility specs
  • Partnered with engineers on the CNN + NLP model to translate AI outputs into legible UI
  • Created Pomu's brand identity — name rationale, logomark, visual system, social templates

My CS background meant I could participate in technical decisions, not just receive them.

FigmaProtoPieMiroTypeformNotionCNN + NLP pipelineWCAG 2.1 AA
II · Research & Insight

We Interviewed 12 Founders. The Same Four Frustrations Kept Surfacing.

Before drawing a single screen, I ran discovery interviews with early-stage fashion founders across New York and Los Angeles. Sessions were semi-structured around three prompts: describe your current process for finding suppliers, walk me through your last bad experience, and what would make you trust a platform recommendation.

Affinity mapping across 12 sessions produced four clusters that shaped every subsequent design decision.

Research affinity map

Fig. 2 · Affinity mapping across 12 discovery interviews. Four friction clusters emerged and guided the entire design strategy.

8 /12 founders Vocabulary Barrier couldn't articulate MOQ or capability needs 11 /12 founders Trust Deficit no reviews, no verified credentials, no recourse 9 /12 founders Platform Fragmentation discovery split across Reddit, trade shows, DMs 12 /12 founders Comparison Paralysis no common schema to compare supplier options RESEARCH SYNTHESIS · 12 INTERVIEWS · 4 FRICTION CLUSTERS

Fig. 3 · Research synthesis visualization — friction clusters extracted from 12 founder interviews. Every product decision mapped back to at least one of these four clusters.

"
I know exactly what fabric I need and what the product should look like. I just have no idea how to translate that into something a factory in Vietnam will understand.
Early-stage streetwear founder, NYC — Interview 04
"
We lost $8,000 to a supplier on Alibaba who ghosted us after the deposit. There was nothing to verify they were legitimate.
Sustainable knitwear brand founder — Interview 07

The Key Insight That Changed Our Direction.

Early assumptions pointed toward a search problem. What the research surfaced was a translation problem. Founders had clear creative vision and zero manufacturing vocabulary. Existing platforms demanded expertise they didn't have before they could even begin searching.

This reframe drove the core product hypothesis: what if the search experience was designed around the user's mental model, not the supplier's catalogue structure? A founder should be able to upload a reference image of a coat they admire, type "I want something like this in organic cotton, under 200 units," and get back ranked matches without knowing a single technical term.

III · Design Goals

Two principles. Every screen had to satisfy both.

Principle 01

Meet Users Where Their Knowledge Ends

Accept natural-language descriptions and visual references as first-class inputs. Translate them into structured queries so founders never have to learn the system's vocabulary.

Principle 02

Surface Trust, Don't Assume It

Every supplier card had to show verifiable signals — certifications, MOQ ranges, turnaround windows, capability tags. Confidence scores on AI matches had to be visible and explainable, not hidden.

IV · Design Process

Information Architecture: Mapping the Journey from Intent to Match

Before any screen-level work, I mapped the full user journey end to end. The key structural challenge was progressive disclosure: show enough to build confidence at each step without overwhelming a founder who is encountering manufacturing discovery for the first time.

ONBOARDING Smart intake — brand stage, product type, rough volume DUAL SEARCH Image upload + keyword input processed by CNN + NLP pipeline RANKED RESULTS Confidence scores, filter panel, transparent match reasoning SUPPLIER PROFILE Verified capabilities, certifications, portfolio samples, direct contact CONNECT In-platform messaging with translation assist, sample request flow END-TO-END PRODUCT FLOW · POMU PLATFORM

Fig. 4 · Information architecture — full product flow from onboarding to supplier connection. Progressive disclosure structured each stage to reduce cognitive load.

Information Architecture Diagram

Fig. 5 · Figma IA diagram — guided search-to-match journey with decision nodes and edge cases mapped.

Designing the Dual-Input Search Experience

The most complex design challenge was the search interface itself. Founders needed two simultaneous inputs — a visual reference and a natural-language description — processed together by the backend AI. The interface had to feel like one coherent action, not two separate inputs bolted together.

Three core decisions shaped the final search pattern:

  • Co-located inputs: the image upload zone and text field share a single container, making the dual nature of the search feel intentional rather than additive
  • Confidence signaling: each result card shows an AI match score with a brief rationale. Founders can see why Pomu recommended a supplier, not just that it did
  • Graceful degradation: edge cases — low-confidence matches, unreadable image uploads, ambiguous queries — each received distinct UI states rather than generic errors
AI Design Challenge

Making AI Feel Transparent, Not Magical

The CNN extracts garment attributes from uploaded images. The NLP layer processes text descriptions. Together they generate a ranked supplier list. My design problem: how do you show a user that the AI understood them, without exposing technical internals?

  • Extracted image attributes surfaced as editable tags beneath the upload zone
  • NLP-detected terms highlighted inline in the text field
  • Confidence score with one-sentence match rationale on each result card
  • Explicit "refine your search" path when confidence is low
Search InterfaceResults Page

Fig. 6–7 · Search interface (left) and results page (right). Co-located inputs, confidence scoring, and transparent match reasoning across both surfaces.

POMU demo — onboarding to results

Fig. 8 · Prototype demo — onboarding intake through to ranked results page with dual-input AI search in action.

POMU Mobile Mockup
POMU Mobile Screens

Fig. 9–10 · Mobile experience — full responsive layout across authentication, onboarding, search, and results surfaces. WCAG 2.1 AA contrast verified throughout.

AI SEARCH INTERACTION MODEL USER INPUT Image Upload reference garment photo mood board clipping Text Description "organic cotton, relaxed fit" under 200 units, US-based AI PIPELINE CNN Image Analysis extracts garment attributes, color, silhouette NLP Text Processing intent extraction, constraint mapping Ranked Match Engine confidence-scored supplier list UI DESIGN LAYER (MY RESPONSIBILITY) Extracted Attribute Tags editable inline — user can correct AI interpretation Confidence Score visible per result with one-sentence rationale Graceful Degradation States low confidence · unreadable image · ambiguous query each state has distinct UI and a clear recovery path THE DESIGN CHALLENGE WAS MAKING AI OUTPUTS LEGIBLE AND TRUSTWORTHY TO NON-TECHNICAL FOUNDERS

Fig. 11 · AI search interaction model — how the CNN + NLP pipeline connects to the UI layer I designed. Each AI output required a corresponding UI pattern to make it legible to non-technical users.

V · Design System

A System Built for a Team That Would Grow Beyond Me.

As the sole designer working alongside three engineers, I knew that every hour spent re-explaining a component decision was an hour the product wasn't shipping. I built the design system not just as a Figma library but as a shared contract — a single source of truth that engineers could pull from directly.

The output was a 40% reduction in time to ship new product surfaces. That number comes from comparing pre-system and post-system velocity tracked in our sprint reviews.

What I Built

  • Component library — buttons, cards, form inputs, overlays, modals, toasts, empty states, with interactive variant logic across all states
  • Token architecture — color, spacing, radius, shadow, and typography tokens named semantically so engineers pulled variables, not hex codes
  • WCAG 2.1 AA specs — every color pair documented with contrast ratios; touch targets sized to 44px minimum across mobile surfaces
  • Flexible grid system — 12-column desktop, 4-column mobile, with responsive breakpoints and spacing rules
DESIGN SYSTEM ARCHITECTURE LAYER 1 · TOKENS Color · Typography · Spacing · Border Radius · Shadow · Motion · Iconography scale Named semantically: --color-brand-primary, --spacing-md, --radius-card — engineers reference tokens, not raw values LAYER 2 · BASE COMPONENTS Button · Input · Select · Checkbox · Badge · Avatar · Tag · Divider · Spinner · Toast Each component carries all interactive states: default · hover · active · disabled · loading · error LAYER 3 · COMPOSITE PATTERNS Search Bar · Supplier Card · Confidence Score Band · Filter Panel · Onboarding Step · Empty State · Error State Assembled from base components. Documented with layout rules, spacing rationale, and copy guidelines per pattern OUTCOME: 40% REDUCTION IN PRODUCTION TIME FOR NEW SURFACES · TRACKED ACROSS SPRINT VELOCITY

Fig. 12 · Design system architecture — three-layer model from tokens through composite patterns. Each layer reducedengineering interpretation time by giving explicit specifications.

ComponentsTypography
ColorGrid
Design SystemColor Palette

Fig. 13 · Design system — components, typography scale, color tokens, grid, and assembled patterns.

VI · Brand Identity

Building a Brand Identity from Scratch for Two Very Different Audiences.

Pomu needed to appeal simultaneously to independent fashion founders and to manufacturing operators. Neither audience shared a visual language or trust vocabulary. The brand had to feel modern and credible to a Brooklyn streetwear founder and professional and reliable to a manufacturing director in Hanoi.

Every decision was justified against both audiences: the name, the mark, the palette, the motion language. Pomu — a coined word combining "polymer" and "mu" (the Japanese character for nothingness, evoking clean possibility) — gave us a blank slate to build meaning onto rather than inheriting industry baggage.

  • Logomark: geometric, scalable, works at favicon size and on trade show signage
  • Color system: high-contrast professional palette with an accent that reads as modern without skewing fashion-industry generic
  • Motion language: transitions that feel precise and intentional — reflecting the reliability a founder needs from a manufacturing partner
POMU Brand Identity

Fig. 14 · Brand identity system — logomark, wordmark, and visual guidelines. Designed to scale across both founder-facing and supplier-facing surfaces.

Social Presence and Marketing Templates

To drive early awareness before full platform launch, I designed a LinkedIn content system that allowed the team to publish consistently without individual design bottlenecks. The template system covered announcement posts, founder spotlight formats, and product update cards — all derivable from the brand token set without opening Figma.

Result: 350+ LinkedIn followers from zero within three months of launch. The template system removed one production bottleneck and let the founding team ship content at startup velocity.

LinkedIn Template 1LinkedIn Template 2

Fig. 15–16 · LinkedIn campaign templates — primary and secondary content layouts built from the brand token set. Non-designers on the team could publish on-brand content without design intervention.

VII · Reflections

What This Project Taught Me About Product Design at Zero.

Designing Pomu taught me that zero-to-one work is fundamentally about prioritization under uncertainty. There are no existing patterns to validate against. Every decision is a bet, and the quality of your research determines how well-placed those bets are.

Working inside an AI pipeline changed how I think about user mental models. The system's capabilities were fixed; the design was the translation layer. Learning to make AI outputs feel comprehensible, not magical, is now a core part of how I approach any ML-adjacent product surface.

The 40% production time reduction from the design system confirmed something I believed but hadn't yet proven at scale: the best design system is one that encodes decisions, not just assets. Every token name, every spacing rule, every component variant was a decision that didn't need to be re-made in a sprint.

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