AI KindleMint Engine V2.0 - Memory-Driven Publishing

A world-class, autonomous Memory-Driven Publishing Engine that transforms from random book creation to intelligent, profit-seeking automation. This system learns from sales data, identifies profitable niches, and generates revenue autonomously through complete end-to-end KDP publishing automation.

🚀 Features - V2.0 Memory-Driven Engine

🧠 Intelligence Layer

• Memory-Driven Niche Selection: DynamoDB brain stores book performance data with ROI calculations
• AI Persona Market Validation: Prevents low-viability content creation with confidence scoring
• Profitable Topic Generation: CTO Agent targets high-performing niches based on historical data
• Data-Driven Marketing: CMO Agent uses proven angles from successful campaigns

🏭 Autonomous Factory

• Complete Content Generation: 8-chapter books with intelligent structure
• AI Cover Generation: Multiple providers (DALL-E 3, Gemini, Stability AI) with fallbacks
• Asset Packaging: KDP-ready manuscript and cover preparation
• Quality Validation: Automated content and asset verification

🚀 Shipping Department

• Automated KDP Publishing: Playwright-based browser automation for zero-touch publishing
• Complete Metadata Management: Title, description, keywords, pricing automation
• Live Amazon Integration: Direct publishing to Amazon KDP marketplace
• Publication Monitoring: Real-time status tracking and error handling

📊 Learning Loop

• Sales Data Ingestion: CFO Agent processes KDP reports automatically
• ROI Calculation: Continuous performance analysis and profit optimization
• Memory Updates: System learns and improves from every book published
• Niche Domination: Focuses resources on proven profitable categories

🏗️ Architecture - Serverless & Scalable

System Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   EventBridge   │───▶│  Lambda Function │───▶│   DynamoDB      │
│   (Scheduler)   │    │  (Orchestrator)  │    │   (Memory)      │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                │
                                ▼
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│      S3         │◀───│  KDP Publisher   │───▶│   Amazon KDP    │
│  (Assets)       │    │   (Playwright)   │    │  (Live Books)   │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Core Components

🧠 Memory System (kindlemint/memory.py)

• DynamoDB Table: KDP_Business_Memory
• Book Records: topic, niche, creation_date, sales_count, ROI
• Performance Analytics: Top-performing niches identification
• Learning Loop: Continuous improvement through sales data

🤖 AI Agents

• CTO Agent (kindlemint/core/generator.py): Memory-driven content generation
• CMO Agent (kindlemint/agents/cmo.py): Data-driven marketing copy
• CFO Agent (kindlemint/agents/cfo.py): Financial analysis and ROI tracking
• Market Validator (kindlemint/validation/market_research.py): AI persona validation

🚀 Publishing Engine

• KDP Publisher (kindlemint/publisher/kdp_agent.py): Automated browser-based publishing
• End-to-End Pipeline (scripts/publish_book_end_to_end.py): Complete orchestration
• Asset Management: Automated cover generation and manuscript preparation

☁️ AWS Infrastructure

• Lambda Functions: Serverless execution environment
• DynamoDB: NoSQL database for memory storage
• S3 Buckets: Asset storage and KDP report ingestion
• EventBridge: Scheduled autonomous execution
• CloudWatch: Logging and monitoring

🛠️ Installation & Setup

Prerequisites

• AWS Account with programmatic access
• Python 3.11+
• OpenAI API key
• KDP Publisher account

Local Development Setup

# Clone the repository
git clone https://github.com/IgorGanapolsky/ai-kindlemint-engine.git
cd ai-kindlemint-engine

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt
pip install -r publisher_requirements.txt

# Install Playwright for browser automation
playwright install chromium

AWS Infrastructure Deployment

# Configure AWS credentials
aws configure --profile kindlemint-keys

# Create DynamoDB table
aws dynamodb create-table \
  --table-name KDP_Business_Memory \
  --attribute-definitions AttributeName=book_id,AttributeType=S \
  --key-schema AttributeName=book_id,KeyType=HASH \
  --billing-mode PAY_PER_REQUEST \
  --profile kindlemint-keys

# Deploy Lambda function
cd lambda/deployment
./deploy-kdp-ingestor.sh

🔧 Configuration

Create a .env file in the root directory with your credentials:

# Essential for AI operations
OPENAI_API_KEY=your_openai_api_key

# Required for KDP publishing
KDP_EMAIL=your_kdp_email
KDP_PASSWORD=your_kdp_password

# Optional for monitoring
SLACK_WEBHOOK_URL=your_slack_webhook_url

# AWS credentials (configured via AWS CLI)
# AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY handled by profile

🚦 Usage

Autonomous Production Operation

# Complete memory-driven pipeline (recommended)
python scripts/publish_book_end_to_end.py --headless

# Force specific niche
python scripts/publish_book_end_to_end.py --niche "productivity" --headless

# Memory-driven content generation only
python scripts/generate_memory_driven_book.py

AWS Lambda Execution

# Manual trigger via AWS CLI
aws lambda invoke \
  --function-name kindlemintEngineFn \
  --payload '{"topic": "Custom Book Topic", "source": "manual"}' \
  --profile kindlemint-keys \
  response.json

# View execution results
cat response.json

Testing and Validation

# Run integration tests
python tests/test_end_to_end_pipeline.py

# Test memory system
python examples/memory_demo.py

# Test individual components
python kindlemint/notifications/slack_notifier.py

📊 Monitoring & Analytics

CloudWatch Logs

• Function: /aws/lambda/kindlemintEngineFn
• Real-time pipeline execution monitoring
• Error tracking and debugging

DynamoDB Memory Analytics

from kindlemint.memory import KDPMemory

memory = KDPMemory()
top_niches = memory.get_top_performing_niches(limit=5)
print(f"Most profitable niches: {top_niches}")

Slack Notifications

• Pipeline start/completion alerts
• Error notifications
• Revenue milestone notifications
• Daily/weekly performance summaries

🤝 Contributing

Contributions are welcome! Please read our Contributing Guidelines for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with ❤️ for self-published authors
• Powered by modern AI and automation technologies