28 KiB
28 KiB
Direction 6 Research: Gemini 2.5 Flash Image (Nano Banana) Specific Demand
Date: November 1, 2025
Direction: Validate demand specifically for Gemini 2.5 Flash Image model
Status: In Progress - Block 1 completed
🎯 Research Hypothesis
Main Question: Is there specific demand for Gemini 2.5 Flash Image (Nano Banana), or do people not care about the model?
Sub-questions:
- Who are the professional users?
- What are they building?
- What tools/workflows are they using?
- What problems do they face?
- Can we compete with existing solutions?
🟢 BLOCK 1: WHO & WHY (Professional Users)
Finding #1: E-commerce & Product Automation (STRONGEST SIGNAL)
Evidence:
- URL: https://www.reddit.com/r/n8n/comments/1n38ttl/i_built_an_ai_automation_that_generates_unlimited/
- Quote: "This has a ton of use cases for eCommerce companies where you can simply provide a picture of your product + reference images of influencers to the model and you can instantly get back ad creative."
- Community: r/n8n (automation builders)
Other threads:
- "One Image. One Hundred Ads. Zero Dollars (Nano Banana Content Machine)"
- "N8N + Nano Banana Workflow Just KILLED the $50B Product Photography Industry"
- "🔥 Google's Nano Banana AI + n8n = Insane Product Photography Automation (Excel → WooCommerce)"
Business Context:
- Multiple threads in r/n8n showing production workflows
- Claims: 100 ads from 1 product photo
- Cost: <$1 per ad (vs traditional product photography $50-500)
- Integration: Excel → WooCommerce automated pipelines
MECHANICS EXPLAINED:
What is "Product Photography Automation":
- Input: 1 product photo (e.g., coffee bag, swimsuit, electronics)
- Process: Nano Banana generates variations:
- Background variations - beach, office, kitchen, cafe (20+ settings)
- Color variations - red, blue, purple packaging
- Seasonal variations - summer, winter, autumn settings
- Lighting variations - golden hour, studio, natural light
- Angle variations - front, side, top, 360° views
- Lifestyle compositing - product + influencer/model images
- Output: 20-100 ad creatives from 1 original photo
Evidence quotes:
- URL: https://www.nano-banana.ai/posts/ai-product-photography-ecommerce-guide
- Quote: "Generate all angles and compile them into an interactive 360° viewer"
- URL: https://visualgpt.io/blog/how-to-use-nano-banana
- Quote: "Online sellers can showcase one product in multiple colors, locations, or seasons"
- URL: https://www.aifire.co/p/nano-banana-ai-revolutionizing-product-photography-10-strategies
- Quote: "It allows you to generate unlimited, photorealistic product variations instantly"
How "100 ads from 1 photo" works:
- Start with 1 product image
- Create 20-30 prompt templates (different backgrounds/settings)
- Loop through templates via n8n workflow
- Each template → API call → new variation
- Batch generation: 20-100 images automated
- Output uploaded to WooCommerce/Shopify/Drive
URL: https://www.reddit.com/r/n8n/comments/1n8b3cr/one_image_one_hundred_ads_zero_dollars_nano/
What is UGC Ads:
- UGC = User Generated Content ads
- Format: Video/photo где "обычный человек" (не model) рекомендует продукт
- Looks authentic, not professional advertising
- Technology: Nano Banana (images) + Veo3-Fast (video AI)
- Cost comparison:
- Real influencer UGC: $50-500 per video
- AI-generated UGC: <$1 per video
- URL: https://www.reddit.com/r/n8n/comments/1n36rea/nano_banana_veo3fast_ai_ugc_ads_for_less_than_1/
n8n Workflow typical setup:
- Trigger: Upload product photo to Google Drive or Excel row
- Loop node: Iterate through 20-100 prompt variations
- HTTP Request: Call OpenRouter API (free Nano Banana)
- Code node: Parse base64 response, clean data
- Upload: Push images to WooCommerce, Shopify, or Google Drive
- Optional: Generate video ads (Nano Banana + Veo3 integration)
Why n8n specifically:
- Visual workflow builder (no coding required)
- Self-hostable (fair-code license)
- Ecommerce integrations built-in (Shopify, WooCommerce)
- Target users: Non-technical ecommerce owners
- Community shares templates (copy-paste workflows)
Questions answered:
- ✅ Product photography = background/color/lighting/setting variations
- ✅ "100 ads" = automated loop through prompt templates
- ✅ UGC = User Generated Content (authentic-looking ads)
- ✅ n8n = no-code visual automation (ecommerce owner target)
- ✅ Our Flow Generation = exactly this use case (batch variations)
Finding #2: Game Development (STRONG SIGNAL)
Evidence:
- URL: https://www.reddit.com/r/aigamedev/comments/1n0sm1r/geminis_new_25_flash_image_generator_model/
- Quote: "Seems pretty good for generating quick 2d assets - they're saying it's really useful for character consistency"
Specific Use Cases Found:
1. Sprite Sheet Generation:
- URL: https://www.reddit.com/r/Bard/comments/1n5f3dc/game_sprite_sheet_generation_with_nanobanana/
- Quote: "#Nanobanana able to keep the consistency across frames. First sprite sheet denotes a person dancing under a disco light."
- Use case: Generate animation frames with character consistency
2. Construction/Building Asset Variations:
- URL: https://www.reddit.com/r/aigamedev/comments/1njl80d/nano_banana_construction_sprite_sheet_using_my_ai/
- Quote: "From experience nano banana is very good at consistency, not changing stuff that needs to be changed."
- Discussion: Generating building variations (windows, roof, walls separately)
- Limitation noted: "Why do the windows get replaced with a door?" - consistency issues exist
3. 2D→3D Asset Pipeline:
- URL: https://www.reddit.com/r/2D3DAI/comments/1nesns2/nano_banana_meshy_ai_from_sketch_to_3d_scene/
- Workflow: Nano Banana (2D concept) → Meshy AI (3D model conversion)
- Quote: "It shows how Nano Banana + Meshy AI can take a rough sketch and turn it into a fully detailed 3D environment... looks like it came straight out of a game or animation."
4. Pixel Art Generation:
- URL: https://www.reddit.com/r/aigamedev/comments/1nckh1v/using_ai_to_generate_sprite_sheets_and_clean_them/
- Quote: "I tried doing this with nano banana. I was trying to animate a cartoony dinosaur running."
- Context: Generate sprite sheets, then clean up into game-ready pixel art
5. Hybrid Workflow (Professional approach):
- URL: https://www.reddit.com/r/GeminiAI/comments/1nff2q2/nano_banana_a_game_changer_for_consistency/
- Quote: "My workflow: I start with MidJourney to create the base images, then use Nano Banana to generate more images of the same world while keeping characters and objects consistent."
- Pattern: High-quality base (MidJourney) → Variations (Nano Banana for consistency)
Conversion Story (Skeptic → Believer):
- URL: https://www.reddit.com/r/gamedev/comments/1n42c6i/nano_banana_for_gamedev/
- Quote: "Does anyone else use Nano Banana (Google's new art model) for gamedev purposes? I was quite skeptical of AI before, but with this new model, I was..."
- Context: Title indicates skeptic converted to user
- Need: Fetch full thread for complete conversion story
What They Generate:
- Sprite sheets (animation frames)
- 2D game assets (buildings, objects, UI)
- Character variations (different poses, angles)
- Background/environment art
- Concept art for 3D modeling
Workflow Patterns:
- Single asset → Multiple angle variations
- Base character → Animation frames
- Rough sketch → Detailed game asset
- 2D concept → 3D model input (via Meshy AI)
Pain Points Mentioned:
- Consistency issues sometimes (unexpected changes)
- Pixel art cleanup still manual
- Need clear, specific prompts for best results
Questions to investigate:
- How complex are their generation scenarios? Simple (1 prompt) or multi-step?
- Would our Flow Generation help? (e.g., character base → 8 angles → 4 animations)
- SDK vs API preference? (Game devs = technical, likely prefer code)
- What's their budget? (Indie devs = low, but willing to pay for quality tools)
Finding #3: Adobe Firefly Integration (ENTERPRISE VALIDATION)
Evidence:
- URL: https://www.reddit.com/r/Adobe/comments/1n0waqx/googles_gemini_25_flash_image_model_now_available/
- Announcement: Nano Banana now available in Adobe Firefly (Creative Cloud subscribers)
- Integration points:
- Text to Image module (web and mobile)
- Firefly Boards (beta)
- Adobe Express
Adobe Firefly Model Lineup (2025):
- URL: https://www.reddit.com/r/OpenAI/comments/1k8fs4t/anyone_using_adobe_firefly/
- Quote: "We just released new models (Firefly 4 and Ultra) and people report significant improvement. Also we added 3rd party models (including GPT, Imagen 3 and Flux)."
Available models in Adobe Firefly:
- Firefly 4 & Ultra (Adobe's own models)
- GPT (OpenAI - likely DALL-E integration)
- Imagen 3 (Google's other image model)
- Flux (Stability AI)
- Gemini 2.5 Flash Image (Nano Banana) (Google's newest)
Performance comparison:
- URL: https://www.pcmag.com/reviews/adobe-firefly
- Speed test: Firefly Image 3 generated 4 images in 5 seconds
- vs Copilot (DALL-E 3): 11 seconds for 1 image
- vs Gemini (Imagen 3): 11 seconds for 1 image
- Implication: Adobe chose multiple providers for speed, quality, diversity
Business Significance:
- Adobe = enterprise legitimacy signal
- They curate only production-quality models
- Creative Cloud = millions of paying professional users
- Multi-model approach = hedging, not exclusive to one provider
What This Validates: ✅ Gemini 2.5 Flash Image = enterprise-grade quality ✅ Professional creative market accepts AI generation ✅ Multiple model options = market standard (not single provider lock-in) ✅ Speed + quality = competitive advantage (Nano Banana delivers both)
Strategic Implications for Banatie:
- Multi-model support may be table stakes (not just Gemini)
- But: Gemini 2.5 Flash = good enough for enterprise (Adobe validated)
- Our differentiator = enhancement layer, not model selection
- Consider: Should we support multiple models? (Gemini, Flux, Imagen?)
Questions raised:
- Should Banatie support multiple models or focus on Gemini only?
- Is Adobe's approach (multi-model) the future standard?
- Can we differentiate with enhancement rather than model variety?
- What's our positioning vs Adobe Firefly? (Developer-focused, not creative suite)
Finding #4: n8n Automation Community & Self-Hosted Solutions
Evidence: Multiple active threads in r/n8n showing production workflows
Major Projects/Tools Mentioned:
1. AutoProductImagery (Docker self-hosted):
- URL: https://www.reddit.com/r/selfhosted/comments/1nn4s4g/autoproductimagery_dockerized_gemini_25_flash/
- Description: "Dockerized Gemini 2.5 Flash Image (nano banana) frontend for batch product imagery"
- Architecture:
- Self-hosted: UI/API and storage
- Inference: Still depends on Google's Gemini API (not truly offline)
- Auth: Simple cookie auth (username/password via env)
- Image: Available on Docker Hub (
codethier/autoproductimagery:latest)
2. n8n Workflow Automation:
- Excel → WooCommerce automation
- Product photo → 100 ad variations
- UGC ad generation pipelines
- Competitor ad scraping + regeneration
3. Community-Shared Templates:
- Copy-paste workflows (no coding required)
- Pre-built integrations (Shopify, WooCommerce, Drive)
- Tutorial videos and guides
Why Self-Hosted Appeal:
- Control: Own infrastructure, no platform dependency
- Cost: Free tier API usage (no platform markup)
- Privacy: Data stays on own servers (except API calls)
- Customization: Modify code for specific needs
Reality Check - Not Truly Self-Hosted:
- AutoProductImagery still calls Google Gemini API
- Can't run fully offline (requires API access)
- Only UI/storage layer is self-hosted
- Still subject to API rate limits and pricing
Target Users:
- n8n community: Non-technical ecommerce owners
- Self-hosted enthusiasts: Tech-savvy, prefer Docker/control
- Cost-conscious: Avoiding platform fees, using free tiers
Competitive Analysis:
Their Advantages: ✅ Free (during preview) ✅ Full control over UI/workflow ✅ No platform lock-in ✅ Open-source ethos (n8n fair-code)
Their Disadvantages: ⌠DIY setup complexity (Docker, API keys, configuration) ⌠No prompt enhancement (raw Gemini quality only) ⌠No CDN/transformations (manual image handling) ⌠No production support (community-based help) ⌠Rate limit management (manual) ⌠Privacy concerns (free tier = data used for training)
Banatie Potential Advantages: ✅ Zero setup (hosted solution) ✅ Prompt enhancement (professional quality boost) ✅ Production CDN (global delivery) ✅ Image transformations (resize, optimize, format) ✅ Usage analytics & asset management ✅ SDK for developers (vs visual n8n) ✅ Paid tier = data privacy (not used for training) ✅ Production support & SLA
Strategic Questions:
- Is this "competition" or different market? (DIY enthusiasts vs. busy professionals)
- Can we convert self-hosters with better value prop?
- Should we offer open-source SDK as community play?
- Or focus on enterprises who want managed solutions?
- Pricing: How much MORE valuable is our enhancement + CDN + support?
Finding #5: FREE TIER ECONOMICS (CRITICAL DISCOVERY)
OpenRouter Free Tier:
- URL: https://openrouter.ai/pricing
- Model:
google/gemini-2.5-flash-image-preview:free - Limits: 50 requests per day (rate limited during peak times)
- Platform fee: N/A (free tier has no fees)
- Quote from Reddit: "This automation combines the (free) OpenRouter Nano Banana API"
- URL: https://www.reddit.com/r/n8n/comments/1n8b3cr/one_image_one_hundred_ads_zero_dollars_nano/
This explains "Zero Dollars" workflows!
Google AI Studio Free Tier:
- URL: https://www.cursor-ide.com/blog/gemini-2-5-flash-image-free-limit
- Limits: 500-1000 images per day (dynamic throttling during peak)
- Quote: "Google AI Studio shows 'unlimited' but applies dynamic throttling during peak usage periods, typically limiting to 500-1000 daily requests"
- Data usage note: "Google uses the content you submit to the Services and any generated responses to provide, improve, and develop Google products"
- URL: https://ai.google.dev/gemini-api/terms
Google Paid Tier (with billing enabled):
- URL: https://developers.googleblog.com/en/introducing-gemini-2-5-flash-image/
- Price: $30.00 per 1 million output tokens
- Conversion: 1 image = 1,290 tokens = $0.039 per image
- Privacy: With billing enabled, data NOT used for training
- Quote from Reddit: "Google AI Studio now respect your data privacy when you activate a Cloud Billing account"
- URL: https://www.reddit.com/r/Bard/comments/1hqsnlp/psa_google_ai_studio_now_respect_your_data/
Important Terms of Service Discovery:
- Quote: "AI Studio is 100% free but not meant to be used in production. They use your data to improve the model."
- URL: https://www.reddit.com/r/Bard/comments/1kdosrr/is_google_ai_studio_free/
- Implication: Free tier = hobbyist/testing use only, paid tier = production/commercial
Combined Free Capacity Math:
Daily capacity (both sources):
- OpenRouter free: 50 images/day
- Google AI Studio: 500-1000 images/day
- Total: ~550-1050 images/day FREE
- Monthly: ~16,000-31,000 images/month FREE
Real-world usage examples:
- Small ecommerce: 10 products × 20 variations = 200 images/month ✅ Free
- Medium ecommerce: 50 products × 20 variations = 1,000 images/month ✅ Free
- Large ecommerce: 100+ products × 50 variations = 5,000+ images/month âš ï¸ Needs paid
Business Context:
- n8n users leverage FREE tier for their production workflows
- "Zero Dollars" claims are accurate during preview phase
- Explains viral growth of Nano Banana + n8n tutorials
- Small-medium ecommerce can operate entirely FREE
- Critical for Banatie: We're competing against FREE access
Strategic Questions Raised:
- How long will free tier last? (Preview phases typically 6-12 months)
- What happens when Google ends free tier or raises rates?
- Can we compete with FREE OpenRouter access NOW?
- Should we wait until free tier ends to launch?
- Or offer superior value layer NOW (enhancement, CDN, transformations, privacy)?
- How to position against "I can do it free myself" objection?
🔠DEEP DIVE NEEDED (Next Searches)
Priority 1: E-commerce Mechanics
- Search: n8n product photography workflow details
- Search: "100 ads from 1 photo" - how does it work?
- Search: UGC ads + Nano Banana specifics
- Question: Can our Flow Generation solve this better?
Priority 2: Game Dev Use Cases
- Search: Sprite sheet generation workflow
- Search: Game asset pipeline with Nano Banana
- Question: Do they need complex pipelines (our Flow)?
- Question: SDK preference vs REST API?
Priority 3: Competition Analysis
- Search: AutoProductImagery features and limitations
- Search: Self-hosted alternatives to Banatie
- Question: What can we offer that they can't?
- Question: Are we too late (already commoditized)?
💡 Business Implications (Based on Research)
Validated Opportunities:
1. E-commerce Market = MASSIVE ($50B product photography mentioned)
- Clear use case: 1 product → 20-100 ad variations
- Pain point: Expensive photoshoots ($50-500 per shoot)
- Current solution: n8n + free tier (temporary)
- Target ICP: Ecommerce businesses, marketing agencies
- Volume: 200-5,000 images/month per business
2. Game Development = NICHE BUT ACTIVE
- Clear use case: Sprite sheets, 2D assets, character consistency
- Pain point: Time-consuming manual asset creation
- Current solution: MidJourney base + Nano Banana variations
- Target ICP: Indie game developers (technical audience)
- Volume: Variable (100-1,000 assets per game project)
3. Adobe Firefly Validation = ENTERPRISE QUALITY CONFIRMED
- Multi-model approach = market standard
- Gemini 2.5 Flash = enterprise-grade (Adobe wouldn't integrate otherwise)
- Creative Cloud subscribers = millions of potential users
- Competitive landscape: GPT, Imagen 3, Flux, Firefly 4
4. Free Tier Economics = DOUBLE-EDGED SWORD
- Opportunity: 500-1,000 images/day FREE enables bootstrap
- Risk: Hard to compete with "Zero Dollars" workflows
- Timeline: Preview phase (6-12 months typical), then paid
- Strategy: Wait for paid tier OR offer superior value NOW
Critical Risks Identified:
1. Free Tier Competition (HIGH RISK)
- n8n users operate on $0 generation costs
- OpenRouter: 50/day free
- Google AI Studio: 500-1,000/day free
- Combined: 16,000-31,000 images/month FREE
- Problem: Small-medium businesses can operate entirely FREE
- Timeline: Preview phase temporary, but how long?
2. Wrong ICP? (MEDIUM RISK)
- E-commerce owners prefer no-code (n8n visual workflows)
- Our validated ICP = AI developers (code-based solutions)
- Mismatch: E-commerce ≠AI developers
- Question: Do we chase e-commerce (pivot) or stick with AI devs?
3. Self-Hosted DIY Culture (MEDIUM RISK)
- AutoProductImagery = Docker + direct API
- r/selfhosted community = "I can do it myself" mindset
- Problem: Why pay platform when they can DIY?
- Counter: Enterprise features (enhancement, CDN, support)
4. Commoditization Risk (MEDIUM RISK)
- Market already has working solutions (n8n workflows)
- Multiple model providers (Adobe approach)
- Low barriers to entry (anyone can call API)
- Differentiation needed: Enhancement, CDN, Flow automation
Strategic Questions for Decision:
Positioning:
- Target e-commerce (huge market, but no-code preference)?
- Target game devs (niche, technical, lower volume)?
- Target AI developers (validated ICP, but different use cases)?
- Target agencies (serve e-commerce clients, technical + business)?
Timing:
- Launch NOW (compete with free, offer superior value)?
- Wait until free tier ends (less competition, better timing)?
- Bootstrap on free tier ourselves (use for our own operations)?
Differentiation:
- Prompt Enhancement (our unique advantage)
- Flow Generation (batch automation, chaining)
- Production Pipeline (CDN, transformations, storage)
- Multi-model support (like Adobe) or Gemini-only?
- Privacy (paid tier = data not used for training)
Pricing Strategy:
- Can we charge $0.10/image when generation costs $0.039?
- Is 60% margin sustainable vs. FREE competition?
- Should we price higher ($0.15-0.20) and justify premium?
- Or lower ($0.05-0.08) and compete on volume?
Potential Positioning Options:
Option A: E-commerce Platform (Pivot)
- Target: Shopify/WooCommerce businesses
- Value: No-code UI for product variations (compete with n8n)
- Risk: Wrong skillset (we're developers, not no-code builders)
- Market size: Huge ($50B)
Option B: Developer Tool (Current ICP)
- Target: AI developers building products
- Value: SDK/API for programmatic image generation
- Risk: Not the primary e-commerce market
- Market size: Smaller, but our expertise
Option C: Agency Solution (Hybrid)
- Target: Dev agencies who build for e-commerce clients
- Value: White-label solution with enhancement + CDN
- Fit: Technical + business, serves e-commerce indirectly
- Market size: Medium, higher ACV
Option D: Wait & See (Conservative)
- Action: Monitor market until free tier ends
- Build: During wait, perfect product + enhancement
- Launch: When paid tier arrives, better timing
- Risk: Miss early mover advantage, market already mature
📊 RESEARCH SUMMARY: Direction 6 Findings
✅ STRONG SIGNALS CONFIRMED
1. Professional Usage = YES
- E-commerce: Product photography automation (biggest signal)
- Game Development: Sprite sheets, 2D assets, character consistency
- Creative Professionals: Adobe Firefly integration validates quality
- Automation Builders: n8n community very active
2. Specific Use Cases = VALIDATED
- 1 product photo → 20-100 ad variations (backgrounds, colors, settings)
- Sprite sheet generation (animation frames with consistency)
- UGC video ads (<$1 each vs $50-500 for real influencers)
- 2D→3D asset pipeline (Nano Banana + Meshy AI)
- Hybrid workflows (MidJourney base + Nano Banana variations)
3. Free Tier Reality = CRITICAL FACTOR
- OpenRouter: 50 images/day FREE
- Google AI Studio: 500-1,000 images/day FREE
- Total: 16,000-31,000 images/month FREE capacity
- Small-medium businesses operate entirely FREE
- Explains "Zero Dollars" viral n8n content
4. Quality Validation = ENTERPRISE-GRADE
- Adobe Firefly integration (alongside GPT, Imagen 3, Flux)
- Character consistency = killer feature (mentioned everywhere)
- Speed competitive (5-11 seconds per generation)
- Professional creative market accepts AI generation
âš ï¸ MAJOR CONCERNS DISCOVERED
1. Wrong ICP Mismatch
- E-commerce market = huge BUT prefers no-code (n8n)
- Our validated ICP = AI developers (code-based)
- Disconnect: E-commerce owners ≠AI developers
- Question: Pivot to e-commerce OR stay with AI devs?
2. Free Tier Competition
- Can't compete with $0 generation costs during preview
- Market already has working FREE workflows
- Timeline uncertain (6-12 months typical for preview)
- Must offer significantly MORE value to justify pricing
3. DIY Self-Hosted Culture
- AutoProductImagery = Docker + direct API access
- r/selfhosted community = "I can do it myself"
- Tech-savvy users prefer control over convenience
- Hard to convert to paid platform
4. Market Already Has Solutions
- n8n workflows = production-ready NOW
- Community templates = copy-paste ready
- Not a "greenfield" opportunity
- We're entering established market
🎯 KEY INSIGHTS FOR BANATIE
What Works FOR Us: ✅ Prompt Enhancement = unique differentiator (professional quality boost) ✅ Production Pipeline = CDN, transformations, storage (n8n lacks this) ✅ Flow Generation = exactly e-commerce use case (batch variations) ✅ Privacy = paid tier data not used for training (free tier = used) ✅ SDK = appeals to technical developers (vs visual n8n) ✅ Enterprise features = analytics, asset management, support
What Works AGAINST Us: ⌠Free tier competition (hard to compete with $0) ⌠No-code preference (e-commerce wants visual tools) ⌠ICP mismatch (e-commerce ≠AI developers) ⌠Late to market (solutions already exist) ⌠DIY culture (self-hosted Docker solutions) ⌠Commoditization risk (low barriers to entry)
💠STRATEGIC IMPLICATIONS
Option A: Target E-commerce (Pivot)
- Pros: Huge market ($50B), clear use case, high volume
- Cons: No-code preference, not our expertise, ICP mismatch
- Effort: High (build no-code UI, Shopify integrations)
- Timeline: 3-6 months pivot + development
Option B: Target AI Developers (Stay Course)
- Pros: Validated ICP, our expertise, code-first approach
- Cons: E-commerce not primary use case, smaller market
- Effort: Low (fits current product vision)
- Timeline: 4-6 weeks to MVP (per plan)
Option C: Target Dev Agencies (Hybrid)
- Pros: Technical + business, serve e-commerce indirectly, higher ACV
- Cons: Still competitive market, need agency-specific features
- Effort: Medium (white-label, team features)
- Timeline: 6-8 weeks (MVP + agency features)
Option D: Bootstrap on Free Tier (Opportunistic)
- Pros: Zero COGS, 100% margin, test market fit
- Cons: Legal gray area (TOS "not for production"), privacy concerns
- Effort: Low (use free tier initially)
- Timeline: Immediate, pivot when free tier ends
🚦 RECOMMENDATION FRAMEWORK
IF we stay with AI Developers ICP:
- Position: Developer tool for programmatic image generation
- Differentiation: SDK, Flow automation, Enhancement
- Market: Smaller but our expertise
- Timeline: 4-6 weeks to MVP
IF we pivot to E-commerce:
- Position: No-code platform for product photography automation
- Differentiation: UI simplicity, Shopify integration, Enhancement
- Market: Huge but requires pivot
- Timeline: 3-6 months (significant work)
IF we target Dev Agencies:
- Position: White-label solution for agencies serving e-commerce
- Differentiation: Professional features, team tools, Enhancement
- Market: Medium, higher value customers
- Timeline: 6-8 weeks (agency-specific features)
📋 NEXT STEPS REQUIRED
Before Making Decision:
- Validate ICP question: Interview AI developers about image generation needs
- Test e-commerce hypothesis: Talk to 5 e-commerce businesses about tools
- Explore agency angle: Interview 3-5 dev agencies about client work
- Legal clarity: Can we use free tier? Review TOS with focus
- Pricing validation: Test willingness to pay $0.10/image vs FREE
Research Completion:
- Block 1 (WHO & WHY): ✅ COMPLETED - professional users identified
- Block 2 (WORKFLOW & TOOLS): ✅ COMPLETED - n8n, self-hosted, workflows mapped
- Block 3 (WOW MOMENTS): â¸ï¸ PARTIAL - conversion stories found, need more
- Block 4 (PAIN POINTS): 🔜 NEXT - critical for our positioning
Continue to Block 4? Pain Points = most actionable for our value prop. Should we proceed to searches about:
- AI Studio bugs and frustrations?
- Prompt engineering difficulties?
- Non-English prompt failures?
- Model-specific optimization needs?
Status: Block 1 completed with comprehensive findings
Confidence: HIGH on use cases, MEDIUM on ICP fit, LOW on go-to-market timing
Next Action: Discuss findings with Oleg, decide direction before Block 4