miirr.ai
From concept to investor-ready demo in 4 weeks — acting as product architect, not just a designer.
The founder of miirr.ai had a compelling vision — using AI to revolutionize fashion buying workflows. But the concept was too abstract to pitch. What they needed wasn't just a design, but a product structure tangible enough for investors to believe in.
Dianne stepped in not as a hired-hand designer, but as a product architect. In four weeks, she went from zero concept to a fully functional interactive prototype, building two complete user flows and the visual foundation the brand still runs on.
Fashion buying involves enormous amounts of manual data work: vendor documents, product specs, order tracking, spreadsheet exports. The status quo is error-prone and time-consuming, but it's what the industry knows. The challenge wasn't just making a better tool — it was making one that felt trustworthy enough for a conservative industry to adopt.
The added pressure: an investor pitch deadline that couldn't move.
Most designers would have spent the first two weeks just learning the vocabulary. Dianne didn't need to.
Her background in retail product data management meant she already understood the pain points of manual entry, the logic of vendor-to-order relationships, and what "a batch export" means in real-world buying contexts. That domain knowledge became the foundation of the information architecture — every AI workflow was mapped against how fashion buyers actually think, not how software engineers imagined they did.
These sessions shaped the core structure: how users upload documents, what the AI processes, and how buying data flows from ingestion to export. The handwritten notes were the first filter — separating what the founder imagined from what users actually needed.
From there, Dianne mapped the real people behind the workflows. Fashion buying isn't one job — it's four distinct roles with different levels of technical comfort, different pain points, and different stakes. Understanding that split was what made the product structure hold up under scrutiny.
The persona work surfaced a critical tension: the tool needed to serve both tech-savvy operators and traditional buyers who still work in spreadsheets. That gap became the design constraint — every feature had to feel simple enough for Emily (a boutique owner handling everything herself) without being underpowered for Priya (scaling data operations across teams). Only then did the work move into Figma.
The IA map clarified the system at a level that gave the engineering team a clear blueprint and the investors a coherent mental model of what they were funding.
Four weeks is not enough time to be precious about process. The approach was deliberately pragmatic: use Figma for early structural planning — flows, hierarchy, key screens — then move directly into Base44 to build the live prototype. No handoff document, no redlines, no translation layer.
Every design decision was immediately testable in a real interface. When something didn't work, it got fixed in the product, not re-drawn in a design file. The speed came from collapsing the gap between "how it should look" and "how it actually behaves."
The core product flow — document upload → AI processing → data review → export — was designed to demystify the "black box" of AI. At every stage, users can see what the system is doing.
The export phase was especially deliberate: two paths (CSV for legacy systems, Shopify for modern e-commerce) directly addressed the split between traditional and digital-native fashion buyers.
For an early-stage AI startup, visual language is a trust signal before it's anything else.
Dianne designed both the product UI and the marketing landing page with a single goal: make miirr.ai look like a serious business partner, not a Silicon Valley experiment. The aesthetic is clean and editorial — confident without being cold. It speaks to tech-savvy users without alienating the conventional buyers the product needs to win over.
The landing page was structured to translate complex AI capabilities into a clear value proposition, positioning miirr.ai as infrastructure for modern fashion buying rather than a novelty feature.
Four weeks in, the founder walked into investor meetings with a fully functional prototype and a coherent visual identity. Two complete user flows — document ingestion and product export — were working end-to-end. The design system built during the sprint became the foundation for all subsequent product development.