For Developers
Enterprise-grade content generation platform built with microservices architecture, AI orchestration, and automated pipelines for scale
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Target Articles
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AI Agents
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Languages
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Layer Hierarchy
Project Overview
Saude do Seu Corpo is an enterprise-grade content generation platform designed to create and maintain 245,000+ medical articles across multiple languages. Built with modern microservices architecture, AI orchestration, and industrial-scale automation.
Architecture Highlights
Microservices Ecosystem
- RESTful API with FastAPI for high-performance endpoints
- Distributed Task Queue using Celery for async processing
- Real-time Monitoring with custom dashboard and health checks
- Containerized Deployment via Docker Compose
- Database Layer with PostgreSQL and SQLAlchemy ORM
- Caching Layer with Redis for performance optimization
AI/ML Orchestration
- Multi-Provider LLM Integration Ollama, Anthropic Claude, OpenAI, Grok, DeepSeek
- Intelligent Agent System 20+ specialized AI agents with CrewAI framework
- Tier-Based Model Selection Adaptive model routing (light/medium/large/heavy)
- Quality Control Pipeline Multi-layer validation and content verification
- Batch Processing Engine Adaptive batch sizing with safety mechanisms
Tech Stack
Backend
- Python 3.11 with Poetry dependency management
- FastAPI for REST API endpoints
- Celery for distributed task processing
- SQLAlchemy for database operations
- Alembic for database migrations
- Pydantic for data validation and serialization
Frontend & Static Site
- Hugo static site generator with multilingual support
- SCSS/Tailwind for responsive design
- JavaScript for interactive features
- Mochawesome for E2E test reporting
Infrastructure
- Docker & Docker Compose for containerization
- PostgreSQL as primary database
- Redis for message broker and result backend
- Nginx for reverse proxy and load balancing
- Terraform for infrastructure as code
DevOps & Quality
- Cypress for E2E testing and SEO validation
- GitLab CI/CD for continuous integration
- Ruff, Black, isort for code quality
- Pytest for unit testing
Key Features
Content Generation Workflow
Task Queueing Priority-based task selection from Redis
AI Processing CrewAI agents generate content using LLMs
Validation Multi-layer quality checks and SEO verification
File Generation Hugo-compatible markdown with frontmatter
Deployment Automated git commit and push
SEO Automation
- Meta Tags Generation Automated title, description, keywords
- Structured Data JSON-LD schema markup for rich snippets
- Internal Linking Intelligent cross-referencing between articles
- Image Optimization Automated compression and lazy loading
- Sitemap Generation Dynamic XML sitemap creation
Testing & Quality Assurance
- E2E Testing Cypress tests for SEO, performance, accessibility
- Performance Monitoring Lighthouse audits and Core Web Vitals
- Content Validation Automated checks for AdSense compliance
- Visual Regression Screenshot comparisons for UI consistency
Performance Metrics
Scale Achievements
245K+ Target Articles
Full medical domain coverage
3 Languages
Portuguese, English, French
20+ AI Agents
Specialized roles for content creation
4-Layer Hierarchy
Comprehensive content organization
Efficiency
- Cost Optimization $5.50 per batch processing
- Adaptive Batching Dynamic sizing based on system load
- Parallel Processing Multi-worker architecture
- Checkpoint Recovery Resume from any point in pipeline
Key Features
Enterprise Dashboard
- Real-time Metrics Live monitoring of content generation progress
- Health Monitoring Comprehensive system health checks (DB, disk, memory, API keys)
- Execution Control Start/pause/resume capabilities for generation and maintenance crews
- Performance Analytics Throughput rates, ETA calculations, health scoring
- Adaptive Batch Control Dynamic batch sizing with safety limits
- Heartbeat System Automatic detection and recovery of stuck workers
Content Generation Pipeline
- Hierarchical Content Structure Macro → Meso → Micro → Nano layers
- Multilingual Support Automated content generation in Portuguese, English, French
- SEO Optimization Automated meta tags, structured data, internal linking
- Quality Assurance Multi-stage validation and review process
- Version Control Git integration with auto-commit functionality
Scalability & Performance
- Horizontal Scaling Worker-based architecture for parallel processing
- Fault Tolerance Checkpoint system with resume capabilities
- Error Handling Comprehensive retry mechanisms and error recovery
- Rate Limiting API throttling and resource management
- Caching Strategy Multi-layer caching for optimization
Automated Pipelines
Content Generation Workflow
Task Queueing Priority-based task selection from Redis
AI Processing CrewAI agents generate content using LLMs
Validation Multi-layer quality checks and SEO verification
File Generation Hugo-compatible markdown with frontmatter
Deployment Automated git commit and push
SEO Automation
- Meta Tags Generation Automated title, description, keywords
- Structured Data JSON-LD schema markup for rich snippets
- Internal Linking Intelligent cross-referencing between articles
- Image Optimization Automated compression and lazy loading
- Sitemap Generation Dynamic XML sitemap creation
Testing & Quality Assurance
- E2E Testing Cypress tests for SEO, performance, accessibility
- Performance Monitoring Lighthouse audits and Core Web Vitals
- Content Validation Automated checks for AdSense compliance
- Visual Regression Screenshot comparisons for UI consistency
Performance Metrics
Scale Achievements
245,000+ Target Articles
Full medical domain coverage
3 Languages
Portuguese, English, French
20+ AI Agents
Specialized roles for content creation
4-Layer Hierarchy
Comprehensive content organization
Efficiency
- Cost Optimization $5.50 per batch processing
- Adaptive Batching Dynamic sizing based on system load
- Parallel Processing Multi-worker architecture
- Checkpoint Recovery Resume from any point in pipeline
Development Workflow
Code Quality Standards
- Type Hints Fully typed Python codebase
- Linting Ruff for fast Python linting
- Formatting Black for consistent code style
- Import Sorting isort for organized imports
- Testing Pytest with high coverage targets
CI/CD Pipeline
- Automated Testing Run on every commit
- Code Quality Checks Linting and formatting validation
- Docker Builds Automated container image creation
- Deployment Automated staging and production releases
Technical Challenges Solved
AI/ML Engineering
- Multi-Provider Orchestration Seamless switching between LLM providers
- Context Management Efficient token usage and context window optimization
- Quality Control Automated content validation and refinement
- Cost Optimization Intelligent model selection based on task complexity
Backend Engineering
- Distributed Systems Celery-based task queue with Redis
- Database Design Normalized schema with efficient queries
- API Design RESTful endpoints with proper versioning
- Monitoring Custom dashboard with real-time metrics
DevOps
- Containerization Multi-service Docker Compose setup
- Orchestration Service discovery and health checks
- Logging Structured logging with color-coded output
- Error Handling Comprehensive exception tracking
Integration Capabilities
External Services
- Google AdSense Revenue optimization with compliant ad placement
- Search Engines Meta search integration via SearxNG
- Analytics Performance tracking and user behavior analysis
- CDN Integration Asset delivery optimization
API Endpoints
- Dashboard Statistics Real-time progress and metrics
- Health Checks System status and component monitoring
- Execution Control Start/pause/resume operations
- Content Management CRUD operations for articles
Business Impact
Scalability
- Built to handle 245,000+ articles with room for expansion
- Microservices architecture allows independent scaling of components
- Cloud-ready design for easy deployment on AWS, GCP, or Azure
Maintainability
- Clean Architecture Separation of concerns and SOLID principles
- Documentation Comprehensive code comments and API docs
- Testing High test coverage for reliability
- Monitoring Real-time health checks and alerting
Cost Efficiency
- Optimized LLM Usage Smart model selection to minimize costs
- Batch Processing Efficient resource utilization
- Caching Strategy Reduce redundant API calls
- Automated Workflows Minimize manual intervention
Skills Demonstrated
Backend Development
- Python expertise with modern frameworks (FastAPI, Celery)
- Database design and optimization (PostgreSQL, SQLAlchemy)
- RESTful API development with proper standards
- Microservices architecture and distributed systems
AI/ML Engineering
- Large Language Model integration and orchestration
- Prompt engineering and context optimization
- Multi-agent systems with CrewAI
- Cost optimization for AI workloads
DevOps & Infrastructure
- Docker containerization and orchestration
- CI/CD pipeline design and implementation
- Infrastructure as Code with Terraform
- Monitoring and observability systems
Full-Stack Capabilities
- Static site generation with Hugo
- Frontend development (HTML, SCSS, JavaScript)
- SEO optimization and web performance
- E2E testing with Cypress
Project Status
🚀 Active Development - Continuous Improvement
Recent Achievements
- Multi-language content generation pipeline
- Real-time monitoring dashboard with health checks
- Adaptive batch processing with safety mechanisms
- Automated SEO optimization and internal linking
- Comprehensive E2E testing suite
Future Roadmap
- Advanced A/B testing for content optimization
- Machine learning for content performance prediction
- Enhanced multilingual support with cultural adaptation
- API rate limiting and advanced caching strategies