Growth Automation Master Plan - AI Research Oracle Edition 🔮

Executive Summary

Ten dokument przedstawia zaktualizowaną strategię automatyzacji wzrostu jako AI Research Oracle - system przewidywania wpływu publikacji naukowych AI na podstawie early signals i machine learning. Pivot z oceny wartości na przewidywanie przyszłości.

Strategia Biznesowa

🔮 Nowe Pozycjonowanie: "The AI Research Oracle"

Stara wizja: Kurator najlepszych badań AI
Nowa wizja: System przewidujący które badania AI będą miały największy wpływ

Dlaczego pivot? - Cytowania pojawiają się po 1-3 latach (nie 48h!) - Early signals (Twitter, GitHub, Mendeley) są dostępne od razu - Nikt inny nie robi predykcji = unikalna pozycja - Weryfikowalność = budowanie zaufania

Cele Biznesowe (zaktualizowane)

Thought Leadership przez unikalną wartość predykcji
Fejm Generation - "firma która przewiduje przyszłość AI"
Lead Generation - researchers i VCs potrzebują tego
Monetization Path - API dla predykcji, premium insights
Exit Strategy - acquisition target dla research platforms

Model Biznesowy

Free Tier: - Weekly top 5 predictions - Monthly accuracy reports - Public prediction tracker

Premium Tier ($49/month): - Daily predictions - API access - Detailed signal breakdowns - Custom field tracking

Enterprise ($499/month): - White-label reports - Custom ML models - Investment recommendations - Slack/Teams integration

🎯 Pipeline 1: AI Research Oracle

Cel

Zbudowanie systemu który przewiduje impact publikacji naukowych w ciągu 7 dni od publikacji, z 70%+ dokładnością.

Architektura w Make.com

Scenario 1.1: Early Signals Collector (NEW!)

Schedule (Every 6 hours)
  ↓
ArXiv API → Get new papers
  ↓
For each paper:
  ├── Semantic Scholar → Author h-index
  ├── Twitter API → Mentions in 24h
  ├── GitHub API → Implementation repos
  └── Altmetric → Early readership
  ↓
Calculate Early Signals Score (0-100)
  ↓
Airtable (Research_Papers)

Scenario 1.2: ML Prediction Engine (NEW!)

Schedule (Daily at 10:00 UTC)
  ↓
Get papers with score > 60
  ↓
Extract features:
  - Author metrics
  - Social signals
  - Content features
  ↓
ML Prediction API
  ↓
Generate predictions:
  - Citations at 1/3/5 years
  - Percentile ranking
  - Breakthrough probability
  ↓
Save predictions

Scenario 1.3: Oracle Content Generator

Schedule (Sunday 10:00)
  ↓
Get top predictions of the week
  ↓
Claude API → Generate:
  - Prediction rationale
  - Impact analysis
  - "Why this matters"
  ↓
DALL-E 3 → Prediction visualization
  ↓
Prepare multi-channel content

Scenario 1.4: Prediction Tracker & Validator

Schedule (Monthly)
  ↓
Get predictions > 1 year old
  ↓
Check actual citations
  ↓
Calculate accuracy
  ↓
Generate transparency report
  ↓
Update ML model training data

Early Signals Score Algorithm (0-100 points)

// Author Signals (40 pts max)
authorScore = {
  maxHIndex: min(hIndex/2, 15),        // 15 pts max
  topInstitution: hasTop20Uni ? 10 : 0, // 10 pts
  trackRecord: prevBreakthroughs * 5,   // 10 pts max  
  industryAuthor: hasGoogleEtc ? 5 : 0  // 5 pts
}

// Social Buzz (30 pts max)
socialScore = {
  twitterMentions: min(mentions24h/10, 10),  // 10 pts max
  githubStars: min(stars7d/10, 5),          // 5 pts max
  redditScore: min(upvotes/50, 5),          // 5 pts max
  mendeleyReaders: min(readers7d/20, 5),    // 5 pts max
  newsPickup: hasNewsMention ? 5 : 0        // 5 pts
}

// Content Signals (20 pts max)
contentScore = {
  hasCode: mentionsGitHub ? 5 : 0,         // 5 pts
  hasDataset: mentionsDataset ? 3 : 0,     // 3 pts
  claimsSOTA: hasSOTAClaim ? 7 : 0,        // 7 pts
  novelMethod: hasNovelClaim ? 5 : 0       // 5 pts
}

// Topic Momentum (10 pts max)
topicScore = {
  trendingField: fieldGrowthRate * 5,      // 5 pts max
  timelyConcept: matchesTrends ? 5 : 0     // 5 pts
}

Metryki Sukcesu (6 miesięcy) - Oracle Edition

Prediction Accuracy: 70%+ within 20% margin
Papers Analyzed: 10,000+
Public Predictions: 500+
Newsletter Subscribers: 5,000+
API Beta Users: 20+
Media Mentions: 10+ ("The startup predicting AI breakthroughs")

Positioning: "The Crystal Ball of AI Research"

Content Strategy Shift

Od: "This week in AI research"
Do: "These papers will define AI's future"

Weekly Sunday 14:00 UTC
  ↓
Get top 5 predictions
  ↓
Get accuracy stats
  ↓
Generate newsletter:
  ├── Accuracy update
  ├── New predictions
  ├── Success stories
  └── Oracle challenge
  ↓
Send via Beehiiv

Oracle Accuracy Report - transparentność buduje trust
This Week's Predictions - top 5 z confidence scores
Why We Think So - explanation of signals
Beat the Oracle Challenge - reader predictions
Track Record - link do public tracker

Free: Weekly predictions
Pro ($29/mo): Daily predictions + API
Teams ($99/mo): Custom predictions for your field

📱 Pipeline 3: Mobile App Growth Automation

(Pozostaje bez zmian - skupiamy się na Oracle najpierw)

🔗 Integracja Między Pipeline'ami

Top prediction of the week
  ↓
Feature in newsletter with rationale
  ↓
Track which predictions drive signups
  ↓
Optimize prediction presentation

Newsletter reader interested in AI
  ↓
Offer app for daily AI insights
  ↓
Convert to app user
  ↓
Upsell premium features

Oracle → Media

Bold prediction made
  ↓
"Startup predicts next GPT" story
  ↓
Media coverage
  ↓
Credibility + new users

📊 Key Metrics - Oracle Focus

Primary KPIs

Prediction Accuracy Rate
Early Signals Collection Rate
Newsletter → API Conversion
Media Mention Sentiment
Community Prediction Participation

ML Model Metrics

Feature Importance Ranking
Model Version Performance
Training Data Quality
Prediction Confidence Calibration

💰 Budżet i Alokacja Zasobów (Zaktualizowany)

Faza 1 (50$/miesiąc) - Miesiące 1-2

Make.com Core: $9
Basic ML hosting: $20
Domain/hosting: $21
Total: $50

Faza 2 (150$/miesiąc) - Miesiące 3-4

Make.com Teams: $29
Twitter API Basic: $100
ML API hosting: $21
Total: $150

Faza 3 (250$/miesiąc) - Miesiące 5-6

Make.com Teams: $29
Twitter API Basic: $100
Altmetric API: $50
Enhanced ML hosting: $40
Beehiiv Growth: $31
Total: $250

ROI Projection (6 months)

Investment: $1,000
Newsletter subscribers value (5k × $10): $50,000
Speaking engagements (4 × $2,500): $10,000
API early access sales (20 × $99): $1,980
Total value created: $61,980
ROI: 6,098% 🚀

🚀 Harmonogram Wdrożenia - Oracle Roadmap

Miesiąc 1: Foundation - Early Signals

Tydzień 1-2: - ✅ Setup ArXiv crawler - ✅ Author h-index integration
- ✅ Twitter mentions counter - ✅ Early Signals Score algorithm

Tydzień 3-4: - ✅ GitHub implementation tracker - ✅ Basic ML model training - ✅ First 10 predictions - ✅ Public tracker website

Miesiąc 2: Prediction Engine

Tydzień 5-6: - ✅ ML model v2 with more features - ✅ Automated prediction pipeline - ✅ Newsletter automation - ✅ "Beat the Oracle" challenge

Tydzień 7-8: - ✅ API beta launch - ✅ Media outreach - ✅ First accuracy report - ✅ Community building

Miesiąc 3-4: Scale & Optimize

📈 Improve prediction accuracy
🔧 Add more signal sources
💡 Launch premium tiers
🚀 Partnership discussions

Miesiąc 5-6: Expansion

🌍 Cover more research fields
💰 Enterprise features
🤖 Advanced ML models
📊 Investment insights

🛠️ Stack Technologiczny - Oracle Edition

Core Infrastructure

Automation: Make.com
Database: Airtable (moving to PostgreSQL)
ML Hosting: Heroku/Railway → AWS
Newsletter: Beehiiv
Analytics: Mixpanel + Custom

Oracle-Specific Tools

ML Framework: scikit-learn → TensorFlow
Feature Store: Feast (future)
Model Tracking: MLflow
Predictions DB: PostgreSQL
Public Tracker: Next.js + Vercel

API Integrations (Priority Order)

ArXiv API - paper source (free)
Semantic Scholar - author metrics (free)
Twitter API - social signals ($100/mo)
GitHub API - implementation signals (free)
Altmetric - academic buzz ($50/mo optional)

⚠️ Risk Management - Oracle Specific

Technical Risks

Prediction accuracy low → Start conservative, improve over time
API costs explode → Implement strict quotas
ML model drift → Regular retraining schedule

Business Risks

Someone copies idea → Move fast, build moat with data
Predictions very wrong → Embrace transparency, learn publicly
Researchers hostile → Partner with them, not against

Reputation Risks

"Just hype detection" → Show rigorous methodology
Cherry-picking wins → Publish ALL predictions
Gaming the system → Detect and prevent manipulation

📈 Success Metrics - 6 Month Targets

Oracle Performance

Papers analyzed: 10,000+
Predictions made: 500+
Accuracy rate: 70%+
Model versions shipped: 5+

Business Metrics

Newsletter subscribers: 5,000
API beta users: 20
Media mentions: 10+
Revenue: $5,000 MRR

Strategic Wins

Known as "The Oracle": Yes/No
Researchers using us: 50+
VCs citing predictions: 5+
Acquisition interest: 2+ companies

✅ Pre-Launch Checklist - Oracle Edition

Technical Setup

[ ] Early Signals scoring live
[ ] ML model trained on historical data
[ ] Prediction API deployed
[ ] Public tracker website
[ ] Accuracy measurement system

Content Ready

[ ] 20 predictions pre-made
[ ] Launch blog post
[ ] Methodology page
[ ] Press kit
[ ] Social proof (beta user quotes)

Business Setup

[ ] Legal review of predictions
[ ] Pricing strategy finalized
[ ] Support system ready
[ ] Feedback loops built
[ ] Community platform chosen

🎯 Quick Wins - First 48 Hours

Hour 0-8: Deploy Early Signals Collector
Hour 8-16: Make first 5 predictions
Hour 16-24: Launch tracker website
Hour 24-32: Publish first prediction post
Hour 32-40: Reach out to AI journalists
Hour 40-48: Open beta signups

🔮 Long-term Vision (12+ months)

Product Evolution

Oracle API: Industry standard for impact prediction
Oracle Certified: Badge for high-impact papers
Oracle Ventures: Fund that invests based on predictions
Oracle Academy: Teach impact prediction

Exit Opportunities

Acquisition targets: Semantic Scholar, Papers with Code, Elsevier
Strategic value: Research evaluation infrastructure
Network effects: More predictions → better model → more users
Moat: Historical prediction accuracy data

Ten plan przekształca nas z "kolejnego AI newsletter" w "The AI Research Oracle" - jedyne źródło przewidujące przyszłość badań AI. Kluczem jest transparentność, ciągłe uczenie się i budowanie społeczności wokół predykcji. 🔮

Motto: "We don't wait for impact. We predict it."