# Project Gist

We are building a modular ecosystem for a visual moodboarding app.

The app lets users search/discover products, curate them into moodboards, extract colors, and export/share.

It will integrate with design tools like Canva, Figma, and Adobe.

Core principle: SDK-first architecture. Each feature is extracted into reusable SDKs that can be shared across multiple projects.

So far, we have:

- Best Practices SDK → defines coding/repo standards.

- Rubric SDK → scores features/hooks/commands (impact, complexity, reusability, strategic fit).

Upcoming SDKs:

- Spec SDK → feature spec templates + validator.

- Claude Helper SDK → guides Claude workflows (suggestions first, rubric scoring, spec enforcement).

- Theme SDK → tokens, fonts, colors, theme provider.

- Integration Adapters SDK → shared interfaces for Canva/Figma/Adobe.

- Security/Performance SDK → CI scans, perf budgets.

The main app (/apps/moodboard-web) will import these SDKs and provide:

- Search + product discovery

- Drag/drop moodboard building

- Brand tagging + annotations

- Export/share (PNG, integrations)

Docs:

- Every feature/spec requires a Markdown doc + Mermaid diagram in /docs.

- Tech debt tracked in /docs/tech-debt.md.

- Claude instructions live in claude.md per app/SDK.

Workflow:

- Suggestions first → Scored by Rubric → Approved → Spec → Code → Tests/CI → Merge.

The natural order is:

1. Spec SDK → So every feature starts with a doc template (keeps Claude structured).

2. Claude Helper SDK → Automates using rubric-sdk + spec-sdk together, and ensures “suggestions before code.”

3. Theme SDK → App-specific but reusable (tokens, theming, color/fonts).

4. Integration Adapters SDK → Interfaces for Canva/Figma/Adobe plugins.

5. Security/Performance SDK → CI checks (will plug into repo standards).

every feature is built as a separate plugin that can be used on the project

Project Viability: HIGHLY FEASIBLE

• TAM: $52B visual design tools → $71B by 2033, $1T social commerce by 2028
• Revenue Potential: $15-25M ARR by Year 3 (conservative-moderate scenario)
• Two-sided marketplace: Brands get discovered, shoppers discover brands
• Key advantages: SDK-first architecture, 70% faster deployment, multiple revenue streams
• Critical mass: 5K brands + 100K users achievable
• Unit economics: $1,200-6,000 LTV per brand at 70% gross margins

Monetization Strategy:

• Brand side (70%): Premium listings $99-499/mo, performance marketing, 3-5% transaction fees
• Consumer side (20%): Pro features $9.99/mo, affiliate commissions
• SDK licensing (10%): Enterprise licenses, white-label solutions

Go-to-Market:

1. Phase 1: 5 core SDKs + 100 beta brands (Fashion/Home Decor focus)
2. Phase 2: Marketplace launch + major platform partnership (Canva/Figma)
3. Phase 3: AI features + enterprise tier + international expansion

Success Indicators:

• Pinterest model validation: $1.15B quarterly, 83% users purchase from discoveries
• Visual discovery: 30% higher CTR, 27% higher AOV
• Network effects at scale create defensive moat

Core Concept: Magazine-style lookbook creator (not moodboards) - think Polyvore but for 2025

Key User Journeys:

1. Holiday Wardrobe Planning: User inputs "pastel colored shirts and light bottoms with accessories" → AI pulls combinations from brand catalogs → Creates magazine-style lookbook
2. Interior Design: User has "brown leather couch, wants wood coffee tables in teak within budget" → AI curates home accessories lookbook with complementary products
3. Social Publishing: Users can post created lookbooks on social media and create them directly in Canva/Adobe

Business Model Refinement:

• Tiered Subscriptions: Lower plans = limited lookbooks/changes, higher plans = unlimited
• Centralized Purchasing: Buy all products from app with unified tracking
• Brand Onboarding: Integrate brand catalogs via API/CSV/PIM systems

Brand Integration Requirements:

• Critical Fields: Product name, price, color (for optimal discovery)
• Integration Methods: Internal API, CSV uploads, or standard formats
• Data Normalization: Handle different brand catalog formats

AI Personalization:

• Learning System: User likes, priorities, behavior → builds personality profile
• Future Curation: Past experience informs future recommendations
• Discovery Toggle: "Surprise me" mode to break out of preference patterns

Sustainability Focus:

• Anti Fast-Fashion: Integration of brand scoring system
• Ethical Verification: Sustainable fashion verification and rating
• Conscious Commerce: Promote brands with better practices

Market Gap Analysis:

• Polyvore shutdown (2018) left significant void
• Current alternatives lack professional publishing + AI styling
• Market opportunity: $2.23B → $60B AI fashion market by 2034

Product Catalog Integration:

• Common Formats: JSON/XML APIs, CSV/Excel, PIM systems
• Required Fields: name, description, image, brand, SKU, price, color, material, category
• Brand Standards: Schema.org markup, ETIM classification
• Processing: 99.8% accuracy color extraction, automated categorization

Subscription Pricing Trends (2024):

• 42.4% of SaaS companies updated pricing (avg 20% increase)
• Hybrid models: subscription tiers + usage-based elements
• 41% offer 30-day free trials with feature/capacity limits

User Personalization:

• 77% prefer personalized data-based experiences
• Amazon's recommendation engine = 35% of total sales
• AI-driven fashion shopping: $2.23B → $60B by 2034

Sustainable Fashion Verification:

• Fashion Transparency Index: 250 brands ranked on climate/energy
• Good On You: People, Planet, Animals scoring system
• 90% of S&P 500 firms report ESG performance

Visual Layout Reference: Magazine-style "Key Prices" grid layout with mixed product categories in professional presentation format