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sscoble edited this page 2026-05-02 13:41:10 -07:00

WardrobeAI

Clothes from real designers. Picked for you. On your budget.

WardrobeAI is a curated clothing subscription service that sources from small businesses and boutique brands. We learn your personal style through a real conversation (not a multiple-choice quiz), match pieces within your monthly budget, and get better with every box based on your feedback.

Page Description
Home You are here
Architecture System design and tech stack
API Reference Full endpoint documentation
Database Schema Tables, fields, relationships
Product Feature specs and user flows
Business Pricing, affiliates, vendor plan
Roadmap Phase details and exit criteria
Runbooks Deploy, troubleshoot, add products
Decisions Architecture Decision Records
SDLC Development process and review gates

Current Status

Phase 0: Foundation — Validating demand and building infrastructure.

  • ✅ README published
  • ✅ Brand identity defined
  • ✅ Competitive analysis complete
  • ✅ Customer personas drafted
  • ✅ Vendor target list created
  • ✅ Landing page spec complete
  • ✅ MVP curation engine built (PR #31)
  • ✅ Deployment guide written
  • 🔲 Legal compliance checklist
  • 🔲 Waitlist landing page deployed
  • 🔲 50+ waitlist signups

Phase 1: MVP target: May — July 2026

  • 20 founding customers
  • Essential tier ($100/mo)
  • Manual curation with AI assistance
  • Feedback loop operational

Problem Statement

For customers: Shopping is overwhelming. Small designers are hard to find. Mass-market subscriptions feel generic. Style quizzes don't capture nuance.

For small designers: Hard to reach customers without massive ad budgets. Wholesale buyers require large minimum orders.

Our Differentiation

Dimension Others WardrobeAI
Sourcing Mass-market brands (Stitch Fix, Nordstrom) Small business and boutique designers
Pricing Per-item ($40-80/piece) Monthly budget ($100 or $250)
Onboarding Multiple-choice quiz Conversational style interview
Learning "Style Shuffle" that doesn't iterate Structured feedback loop
Constraints One-size-fits-all Invariants — your hard lines honored
Custom Off-the-rack only AI design-on-demand (Phase 3+)

Tech Stack

  • Frontend: Next.js (React), Tailwind CSS, mobile-first
  • Backend: Node.js + Express, SQLite (MVP)
  • AI/ML: Python curation service, LLM for style interview (Phase 2+)
  • Payments: Stripe
  • Hosting: Vercel (frontend) + Railway/Fly.io (backend)
  • Analytics: Umami (self-hosted)
  • Infrastructure: Storj for static assets
  • Source Control: Forgejo at os.scoble.me

Repository

Contributing

All changes go through PR review with required gates:

  1. @engineering — Code quality, architecture
  2. @trust — Security review
  3. @market — Product requirements
  4. @cos — Final approval and merge

See SDLC for full process.