banatie-strategy/09_mvp_scope.md

MVP Scope: Banatie for AI Developers

Date: October 20, 2025 Target ICP: AI-powered developers (Claude Code, Cursor users) Development Timeline: 4-6 weeks Launch Goal: First 5-10 beta users by end of November 2025

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MVP Philosophy

Principle: Build the MINIMUM to validate willingness to pay, not the complete vision.

Goal: Solve ONE core problem exceptionally well:

"Generate production-ready images from Claude Code without context switching"

Not the goal: Build all planned features (Flow, Namespaces, On-demand URL generation)

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Core Value Proposition (MVP)

What users get:

1. MCP integration for Claude Code → generate images without leaving environment
2. Prompt Enhancement → write in any language, get optimized results
3. Production CDN URLs → no manual download/upload/hosting
4. Contextual references → maintain consistency across assets (@logo, @hero)
5. Basic transformations → resize, format, optimize automatically

What users DON'T get in MVP:

• Flow-based chained generation (future)
• Namespaces / project organization (future)
• On-demand generation via URL (future)
• Advanced focal point analysis (future)
• Team collaboration features (future)

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MUST HAVE Features (Launch Blockers)

1. MCP Server for Claude Code ✅ CRITICAL

Why it's critical:

• This is the KILLER FEATURE
• Solves the #1 pain point (context switching)
• No competitor has this
• Differentiates from "just another AI image API"

Functionality:

// MCP Tools to implement:

banatie_generate({
  prompt: string,           // User's prompt (any language)
  name?: string,           // Optional name for reference (e.g., "logo")
  style?: string,          // Optional style preset
  aspectRatio?: string,    // e.g., "16:9", "1:1", "4:3"
  width?: number,          // Target width for optimization
  referenceImages?: string[] // Array of @names to reference
})
// Returns: {
//   url: "https://cdn.banatie.app/...",
//   name: string (if provided),
//   transformations: { ... } (preset URLs)
// }

banatie_upload({
  file: base64 | url,
  name: string  // Required for referencing
})
// Returns: {
//   name: string,
//   url: "https://cdn.banatie.app/..."
// }

banatie_list_images({
  limit?: number
})
// Returns: Array of previously generated/uploaded images

Implementation notes:

• Follow MCP spec: https://modelcontextprotocol.io/
• Test with Claude Desktop + Claude Code
• Provide clear error messages
• Handle API key authentication

Time estimate: 1-2 weeks

---

2. Prompt Enhancement Agent ✅ CRITICAL

Why it's critical:

• Solves #2 pain point (prompt engineering complexity)
• Massive value for non-native English speakers (Russian developers)
• Improves generation quality automatically
• Low-hanging fruit (you already have working version)

Functionality:

Input: User's prompt (any language, casual description)

"маг эльфийской крови в средневековом городе на закате"

Process:

1. Detect language (if not English, translate)
2. Analyze intent and visual elements
3. Apply Gemini 2.5 Flash Image best practices:
   • Camera parameters for photorealism
   • Lighting descriptions
   • Composition guidelines
   • Style keywords
4. Generate optimized English prompt

Output: Production-ready prompt

"A photorealistic portrait of an elven-blooded wizard standing in a medieval European city street at golden hour sunset, warm amber lighting, shot with 85mm lens at f/2.8, shallow depth of field, detailed texture on stone buildings, volumetric light rays, cinematic composition"

Implementation notes:

• Use Gemini 2.0 Flash (fast, cheap)
• Include Google's official guidelines in agent prompt
• Cache common translations
• Show both original + enhanced prompt to user (educational)

Time estimate: 3-5 days (since you already have working version)

---

3. Asset Persistence + CDN URLs ✅ CRITICAL

Why it's critical:

• Solves #3 pain point (manual file management)
• "Production-ready" means hosted + optimized
• No competitor bundles this (they just return base64)

Functionality:

Storage:

• MinIO (S3-compatible) ✅ Already implemented
• Organized by user/project
• Permanent URLs (no expiry)

CDN Delivery:

• Cloudflare CDN integration
• Global caching
• Automatic optimization

URL structure:

https://cdn.banatie.app/u/{user_id}/{image_id}.webp?w=800&q=90

Metadata stored (PostgreSQL):

• image_id (UUID)
• user_id
• original_prompt (user's input)
• enhanced_prompt (after agent)
• generation_params (style, aspect ratio, etc.)
• reference_images (if used)
• name (if provided, for @references)
• created_at
• generation_cost (for billing)

Time estimate: 1 week (storage already done, need CDN setup)

---

4. Basic Image Transformations ✅ IMPORTANT

Why it's important:

• Solves #4 pain point (responsive images, formats)
• Demonstrates "production-ready" value
• Common use case (mobile vs. desktop)

Functionality:

Via URL query parameters:

?w=800           // Width resize
?h=600           // Height resize
?ar=16:9         // Aspect ratio crop
?f=webp          // Format (webp, png, jpg, avif)
?q=85            // Quality (1-100)
?fit=cover       // Fit mode (cover, contain, fill)

Preset transformations (returned in API response):

{
  "url": "https://cdn.banatie.app/u/123/img456.webp",
  "transformations": {
    "mobile": "...?w=400&f=webp&q=85",
    "tablet": "...?w=768&f=webp&q=85",
    "desktop": "...?w=1200&f=webp&q=90",
    "thumbnail": "...?w=150&h=150&fit=cover&f=webp"
  }
}

Implementation:

• Imageflow-Server (as per tech spec) ✅ Planned
• OR Cloudflare Image Resizing (simpler for MVP)
• Cache transformed versions (don't regenerate)

Time estimate: 1 week

---

5. Contextual Asset Referencing (@name) ✅ UNIQUE FEATURE

Why it's critical:

• Solves #5 pain point (consistency across assets)
• UNIQUE to Banatie (no competitor has this)
• Enables powerful workflows (brand consistency, character consistency)

Functionality:

Naming assets:

// Generate and name
banatie.generate("fictional water brand logo", {name: "logo"})

// Upload and name
banatie.upload("./logo.png", {name: "logo"})

Referencing in future generations:

// Use @name in prompt
banatie.generate("product photo with @logo on table")
banatie.generate("hero banner with @logo in nature background")

Behind the scenes:

1. Parse prompt for @references
2. Fetch referenced images from storage
3. Include as image inputs to Gemini API
4. Generate with visual context

Implementation notes:

• Simple regex to find @names in prompts
• Replace @names with actual image references
• Support multiple @references in one prompt
• Show which references were used (transparency)

Time estimate: 3-5 days

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6. REST API ✅ FOUNDATION

Why it's critical:

• MCP is built on top of this
• Enables direct integration for power users
• Future SDK/libraries depend on this

Endpoints:

POST /v1/generate
Body: {
  prompt: string,
  name?: string,
  style?: string,
  aspectRatio?: string,
  width?: number,
  referenceImages?: string[]
}
Response: {
  id: string,
  url: string,
  enhanced_prompt: string,
  transformations: {...}
}

POST /v1/upload
Body: { file: base64, name: string }
Response: { name: string, url: string }

GET /v1/images
Query: ?limit=20
Response: [ {...image objects} ]

GET /v1/images/:id
Response: {...image object with metadata}

DELETE /v1/images/:id
Response: { success: boolean }

GET /v1/account/usage
Response: {
  generations_used: number,
  credits_remaining: number,
  tier: "free" | "credits" | "pro"
}

Authentication:

• API key in header: Authorization: Bearer bnt_xxx
• Rate limiting (by tier)
• Usage tracking (for billing)

Time estimate: 1 week (partially done)

---

7. Simple UI / Playground ✅ IMPORTANT

Why it's important:

• First impression (users want to "see it work")
• Visual proof of quality
• Educational (shows code snippets)
• Low barrier to test

Pages:

1. Homepage (Landing)

• Value prop headline
• 3-step explanation (MCP → Generate → CDN)
• "Try Demo" CTA
• Features overview
• Pricing overview

2. Demo/Playground

• API key input (no registration needed for MVP)
• Prompt textarea (accepts Russian, English, etc.)
• Style dropdown (optional)
• Aspect ratio selector
• Generate button
• Results display:
  • Generated image
  • Original + enhanced prompt (side by side)
  • Transformation previews
  • Code snippets panel (cURL, Python, JS, MCP)
  • Copy-to-clipboard for URLs

3. Dashboard (After API key entered)

• Generation history (last 20)
• Usage stats (generations used, credits left)
• API key management
• Billing / credits (if applicable)

Tech stack:

• Next.js ✅ Already implemented
• Tailwind CSS ✅ Already used
• Simple, developer-focused design (not marketing fluff)

Time estimate: 1 week (refine existing demo UI)

---

8. Credit-Based Payment System ✅ REQUIRED FOR REVENUE

Why it's critical:

• Can't validate willingness to pay without payment system
• Credits model validated in ICP research
• Stripe integration straightforward

Functionality:

Credit packs for purchase:

• $20 = 200 generations (90-day expiry)
• $50 = 600 generations (90-day expiry)
• $100 = 1,500 generations (90-day expiry)

Free tier:

• 10 generations/month
• Resets monthly
• Watermark (SynthID) on images

Payment flow:

1. User clicks "Buy Credits"
2. Stripe Checkout (hosted page)
3. Webhook on success → add credits to account
4. Credits deducted per generation

Stripe setup:

• Products: 3 credit packs
• Webhook handler for checkout.session.completed
• Customer portal (manage payment methods)

Database schema additions:

credits_transactions (
  id, user_id, amount, pack_size,
  expires_at, stripe_session_id, created_at
)

users.credits_balance (integer)
users.credits_expiry (timestamp)

Time estimate: 1 week

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NICE TO HAVE (If Time Permits)

9. @last Reference (Shortcut)

Functionality:

banatie.generate("hero in armor")
banatie.generate("make @last more detailed")  // References previous generation

Why nice-to-have:

• Convenient for iteration
• Simple to implement (just cache last generation ID)

Time estimate: 1-2 days

---

10. Batch Generation (Multiple Prompts)

Functionality:

banatie.generateBatch([
  "hero level 1",
  "hero level 2",
  "hero level 3"
])

Why nice-to-have:

• Useful for game asset generation (Oleg's use case)
• Not critical for initial validation

Time estimate: 2-3 days

---

CUT FROM MVP (Future Roadmap)

❌ Flow-Based Chained Generation

Why cut:

• Complex to build (requires state management, execution engine)
• Hard to explain (cognitive load)
• Niche use case (not every developer needs this)
• Can add after PMF

Future priority: HIGH (after 50+ users)

---

❌ Namespaces / Project Organization

Why cut:

• Users can manage with @names for MVP
• Adds UI complexity (project switcher, settings)
• Not blocking for core workflow

Future priority: MEDIUM (after 100+ users)

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❌ On-Demand Generation via URL

Why cut:

• Clever feature but not core pain point
• Requires caching strategy, URL signing
• Better to validate core workflow first

Future priority: HIGH (cool differentiator, but after PMF)

---

❌ Advanced Focal Point Analysis

Why cut:

• Nice-to-have for auto-cropping
• Not critical if transformations are manual
• Can use basic center-crop for MVP

Future priority: LOW (automation, not essential)

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❌ Style Presets / Fine-Tuning

Why cut:

• Gemini 2.5 Flash Image is already great
• Adds complexity (preset management, UI)
• Prompt Enhancement covers most use cases

Future priority: MEDIUM (after users request specific styles)

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❌ Team Collaboration / Multi-User

Why cut:

• ICP is solo developers
• Adds auth complexity (invites, roles, permissions)
• Can add when agencies become customers

Future priority: MEDIUM (for agency expansion)

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❌ Image Editing / Inpainting

Why cut:

• Out of scope (we're generation, not editing)
• Complex UI (selection tools, masks)
• Gemini supports it, but not MVP focus

Future priority: LOW (different product direction)

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Technical Architecture (MVP)

Backend (Express + Node.js) ✅ Existing

Core services:

• API Gateway (REST endpoints)
• Prompt Enhancement Agent (Gemini 2.0 Flash)
• Image Generation (Gemini 2.5 Flash Image)
• Asset Manager (MinIO integration)
• Transformation Service (Imageflow or Cloudflare)
• Billing Service (Stripe webhooks)

Database (PostgreSQL):

• users (id, email, api_key, credits_balance, tier, created_at)
• images (id, user_id, prompt, enhanced_prompt, url, name, metadata, created_at)
• credits_transactions (id, user_id, amount, expires_at, stripe_session_id)
• api_keys (id, user_id, key_hash, last_used, created_at)

---

Frontend (Next.js) ✅ Existing

Pages:

• / - Landing page
• /demo - Playground (API key input + generation)
• /dashboard - History + usage (after auth)
• /pricing - Credit packs
• /docs - API documentation

Components:

• ImageGenerator (prompt input + results)
• CodeSnippets (cURL, Python, JS, MCP examples)
• TransformationPreview (show different sizes/formats)
• ApiKeyInput (simple auth for demo)

---

Infrastructure

Hosting:

• VPS (Contabo, Singapore) ✅ Existing
• Docker containers (backend + frontend)

Storage:

• MinIO (S3-compatible) ✅ Existing

CDN:

• Cloudflare (free tier OK for MVP)

Payments:

• Stripe (standard integration)

Monitoring:

• Basic logging (PM2 logs)
• Uptime monitoring (UptimeRobot free tier)
• Error tracking (Sentry free tier)

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Development Timeline (4-6 Weeks)

Week 1: Core Generation Pipeline

• Finalize REST API endpoints
• Prompt Enhancement Agent (refine existing)
• Image generation with reference support
• Basic storage + CDN integration

Deliverable: Working API (generate + upload + references)

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Week 2: MCP Implementation

• MCP server setup (follow spec)
• Implement 3 tools (generate, upload, list)
• Test with Claude Desktop
• Documentation for MCP usage

Deliverable: Working MCP integration

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Week 3: Transformations + UI

• Image transformation service (Imageflow or Cloudflare)
• Refine demo UI (code snippets, previews)
• Dashboard (history, usage stats)
• API key management

Deliverable: Functional UI for testing

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Week 4: Payments + Polish

• Stripe integration (credit packs)
• Free tier limits enforcement
• Watermark for free tier
• Landing page copy + design
• API documentation

Deliverable: Monetization-ready product

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Week 5-6: Beta Testing + Iteration

• Invite 5-10 validated users from research
• High-touch onboarding (help with setup)
• Gather feedback, fix bugs
• Iterate on UX pain points
• Prepare for public launch

Deliverable: Product-market fit signals or pivot triggers

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Success Metrics (MVP)

Technical Metrics

• MCP integration works reliably (95%+ success rate)
• Image generation latency <10 seconds (p95)
• CDN delivery fast (global, <500ms)
• API uptime >99%

Product Metrics

• 5-10 beta users onboarded
• 50+ generations completed
• 3+ users generate >10 images (engaged)
• 2+ users purchase credits (willingness to pay validated)

Qualitative Metrics

• "This solves my problem" feedback (3+ users)
• Feature requests are refinements, not fundamental changes
• Users recommend to others
• Low churn (users stick around after trial)

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What "Done" Looks Like (MVP Launch)

A developer can:

1. Install Banatie MCP in Claude Desktop (5 min setup)
2. Use Claude Code to generate a Next.js site
3. Generate images via MCP without leaving Claude Code:

   Human: Create a hero image for this landing page about eco-friendly water bottles
   
   Claude: [calls banatie_generate MCP tool]
   I've generated a hero image and inserted the production CDN URL in the code.
   

4. Reference previous images for consistency:

   Human: Now create a product photo with the same bottle @hero
   
   Claude: [calls banatie_generate with @hero reference]
   Done! Product photo maintains the same bottle design.
   

5. See generated images in demo UI (history, transformations)
6. Copy code snippets (cURL, Python, JS) for direct API use
7. Purchase credits ($20 pack) via Stripe
8. Use credits for additional generations

And it feels:

• Fast (no waiting, instant CDN URLs)
• Seamless (no context switching)
• Professional (production-ready, not prototype)
• Trustworthy (stable, reliable, documented)

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Risk Mitigation

Risk 1: MCP Integration Complexity

Mitigation:

• Study existing MCP servers (examples in repo)
• Test early and often with Claude Desktop
• Provide clear error messages
• Fallback: REST API works even if MCP has issues

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Risk 2: Prompt Enhancement Quality

Mitigation:

• Use Gemini 2.0 Flash (fast, capable)
• Include Google's official guidelines in agent prompt
• Show both prompts to user (transparency)
• Allow user to override/edit enhanced prompt

---

Risk 3: CDN/Transformation Service Complexity

Mitigation:

• Start with Cloudflare Image Resizing (simpler)
• Fallback: Imageflow-Server if Cloudflare insufficient
• Precompute common transformations (cache)

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Risk 4: Payment Fraud / Abuse

Mitigation:

• Stripe handles fraud detection
• Rate limit free tier aggressively (10/month)
• Monitor usage patterns (flag anomalies)
• Require email verification for credits purchase

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Risk 5: Gemini API Costs Exceeding Revenue

Mitigation:

• Track cost per generation (Gemini API fees)
• Ensure pricing covers costs + margin
• Free tier is truly limited (10/month)
• Monitor burn rate daily

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Post-MVP Roadmap (Prioritized)

Phase 2 (After PMF):

1. Flow-based generation (chained workflows)
2. On-demand generation via URL (programmatic)
3. Pro subscription tier (500 gen/month included)
4. Advanced style presets
5. Batch generation

Phase 3 (Agency Expansion):

1. Namespaces / project organization
2. Team collaboration (multi-user)
3. Usage analytics dashboard
4. White-label / reseller options

Phase 4 (Platform Play):

1. Public API marketplace (share custom agents)
2. Community styles / presets
3. Integrations (Figma, Vercel, Netlify)
4. Enterprise tier (SLA, support, SSO)

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Decision Gates

After 4 weeks (MVP complete):

• Beta test with 5-10 users
• If 60%+ engaged → continue
• If <60% engaged → reassess features/ICP

After 6 weeks (Beta feedback):

• If 2+ purchased credits → validated willingness to pay
• If 0 purchases → pricing issue or value unclear
• If bugs/UX issues → iterate 1-2 more weeks

After 8 weeks (Soft Launch):

• Post in r/ClaudeAI, Indie Hackers
• Track sign-ups, usage, conversion
• If $500+ MRR → full launch
• If <$500 MRR → pivot or iterate

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Document owner: @men + Oleg (joint) Status: Draft for MVP development Next review: After validation complete (if GO decision) Related docs: 07_validated_icp_ai_developers.md, 10_pricing_strategy.md