System Architecture Overview

This document provides a comprehensive technical overview of the Cimigo Collect Platform architecture, design decisions, and implementation patterns for developers working on the platform.

🏗️ High-Level Architecture

The Cimigo Collect Platform follows a modular, domain-driven architecture built for scalability, maintainability, and developer experience.

🎯 Design Principles

1. Domain-Driven Design (DDD)

The backend is organized around business domains rather than technical layers:

backend/
├── api/                 # HTTP layer (FastAPI routes)
│   ├── v1/             # API version 1 endpoints
│   ├── public/         # Public endpoints (no auth)
│   └── internal/       # Internal service endpoints
├── core/               # Cross-cutting concerns
│   ├── config.py       # Configuration management
│   ├── security.py     # Authentication & authorization
│   └── exceptions.py   # Custom exception classes
├── models/             # Database entities (SQLAlchemy)
│   ├── tenant.py       # Multi-tenancy models
│   ├── survey.py       # Survey-related models
│   └── response.py     # Response data models
├── repositories/       # Data access layer
│   ├── base.py         # Base repository patterns
│   ├── survey.py       # Survey data operations
│   └── response.py     # Response data operations
├── services/           # Business logic layer
│   ├── survey.py       # Survey management logic
│   ├── response.py     # Response processing logic
│   └── webhook.py      # Webhook delivery logic
└── schemas/            # API contracts (Pydantic)
    ├── survey.py       # Survey request/response schemas
    └── response.py     # Response data schemas

2. Separation of Concerns

Clear boundaries between different layers:

• API Layer: HTTP handling, validation, serialization
• Service Layer: Business logic, domain rules, orchestration
• Repository Layer: Data persistence abstraction
• Model Layer: Database entities and relationships

3. CQRS (Command Query Responsibility Segregation)

Separate read and write operations for optimal performance:

# Commands (Write Operations)
class SurveyService:
    async def create_survey(self, survey_data: SurveyCreate) -> Survey:
        # Business logic for creating surveys
        pass
    
    async def update_survey(self, survey_id: int, updates: SurveyUpdate) -> Survey:
        # Business logic for updating surveys
        pass

# Queries (Read Operations)  
class SurveyQueryService:
    async def get_survey(self, survey_id: int) -> Survey:
        # Optimized read operations
        pass
    
    async def list_surveys(self, filters: SurveyFilters) -> List[Survey]:
        # Optimized listing with caching
        pass

🔧 Technology Stack

Backend Stack (FastAPI + Python)

# Core Technologies
FastAPI >= 0.104.0     # High-performance async web framework
Pydantic >= 2.0.0      # Data validation and settings management
SQLAlchemy >= 2.0.0    # ORM and database toolkit
Alembic >= 1.12.0      # Database migration management
asyncpg >= 0.28.0      # Async PostgreSQL driver
redis >= 5.0.0         # Redis client for caching/queues
celery >= 5.3.0        # Distributed task queue

Why FastAPI?

• ⚡ Performance: One of the fastest Python frameworks
• 🔒 Type Safety: Built-in Pydantic integration for validation
• 📚 Documentation: Automatic OpenAPI/Swagger generation
• 🔄 Async Support: Native async/await for high concurrency
• 📏 Standards: OpenAPI, JSON Schema, OAuth2 compliant

Frontend Stack (Next.js + React)

// Core Technologies
Next.js 14+            // React framework with App Router
React 18+              // UI library with concurrent features
TypeScript 5+          // Type safety and developer experience
Tailwind CSS 3+        // Utility-first CSS framework
React Hook Form 7+     // Performant form handling
Framer Motion 10+      // Animation and micro-interactions
React Query 4+         // Server state management

Why Next.js?

• 🔥 Performance: Server-side rendering and static generation
• 🎯 Developer Experience: Hot reload, error overlay, built-in optimization
• 🌐 SEO Friendly: Server-side rendering for search engines
• 📱 Mobile Optimized: Responsive design patterns
• 🏗️ Full-Stack: API routes for server-side logic

Database Stack (PostgreSQL)

-- Core Capabilities
PostgreSQL 15+         -- Primary database with advanced features
Row Level Security     -- Multi-tenant data isolation
JSONB Support         -- Flexible survey definition storage
Full-text Search      -- Response content search
UUID Primary Keys     -- Distributed-friendly identifiers
Partitioning         -- Large table performance optimization

Why PostgreSQL?

• 🔒 ACID Compliance: Reliable transactions and data consistency
• 📊 JSONB: Flexible survey schema storage with indexing
• 🚀 Performance: Excellent query optimization and indexing
• 🔧 Extensions: Rich ecosystem (pgcrypto, pg_stat_statements)
• 🏢 Multi-tenancy: Built-in Row Level Security (RLS)

Cache & Queue Stack (Redis/KeyDB)

Use Cases:
├── Session Storage    # JWT refresh tokens and user sessions
├── Response Caching   # Frequently accessed survey data
├── Rate Limiting      # API request throttling per tenant
├── Queue Management   # Webhook delivery and email sending
├── Real-time Data     # Survey response streaming
└── Distributed Locks # Preventing duplicate operations

📊 Database Design

Multi-Tenant Architecture

The platform uses shared database, shared schema with Row Level Security for data isolation:

-- Enable RLS on all tenant-scoped tables
ALTER TABLE projects ENABLE ROW LEVEL SECURITY;
ALTER TABLE surveys ENABLE ROW LEVEL SECURITY;
ALTER TABLE responses ENABLE ROW LEVEL SECURITY;
ALTER TABLE links ENABLE ROW LEVEL SECURITY;

-- Create tenant isolation policies
CREATE POLICY tenant_isolation ON surveys
    USING (tenant_id = current_setting('app.current_tenant_id')::uuid);

CREATE POLICY tenant_isolation ON responses  
    USING (tenant_id = current_setting('app.current_tenant_id')::uuid);

Core Entity Relationships

Schema Design Patterns

1. Audit Trail Pattern

-- Every table includes audit fields
CREATE TABLE surveys (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    tenant_id UUID NOT NULL,
    
    -- Audit fields
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW(),
    created_by UUID REFERENCES users(id),
    updated_by UUID REFERENCES users(id),
    version INTEGER DEFAULT 1,
    
    -- Soft delete
    deleted_at TIMESTAMP,
    deleted_by UUID REFERENCES users(id)
);

-- Automatic update trigger
CREATE OR REPLACE FUNCTION update_updated_at_column()
RETURNS TRIGGER AS $$
BEGIN
    NEW.updated_at = NOW();
    RETURN NEW;
END;
$$ language 'plpgsql';

CREATE TRIGGER update_surveys_updated_at 
    BEFORE UPDATE ON surveys
    FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();

2. Flexible Schema Storage

-- JSONB for flexible survey definitions
CREATE TABLE surveys (
    id UUID PRIMARY KEY,
    definition JSONB NOT NULL,
    schema_version TEXT NOT NULL DEFAULT '1.0',
    
    -- JSON Schema validation
    CONSTRAINT valid_definition 
        CHECK (jsonb_typeof(definition) = 'object'),
    
    -- Schema version validation
    CONSTRAINT valid_schema_version
        CHECK (schema_version ~ '^[0-9]+\.[0-9]+$')
);

-- Optimized indexes for JSONB queries
CREATE INDEX idx_surveys_definition_gin ON surveys USING GIN (definition);
CREATE INDEX idx_surveys_schema_version ON surveys (schema_version);
CREATE INDEX idx_surveys_title ON surveys ((definition->>'title'));

3. Event Sourcing Pattern

-- Event log for audit and debugging
CREATE TABLE events (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    tenant_id UUID NOT NULL,
    aggregate_type TEXT NOT NULL, -- 'survey', 'response', etc.
    aggregate_id UUID NOT NULL,
    event_type TEXT NOT NULL,     -- 'created', 'updated', 'deleted'
    event_data JSONB NOT NULL,
    metadata JSONB DEFAULT '{}',
    created_at TIMESTAMP DEFAULT NOW(),
    created_by UUID REFERENCES users(id)
);

-- Indexes for event querying
CREATE INDEX idx_events_tenant_aggregate ON events (tenant_id, aggregate_type, aggregate_id);
CREATE INDEX idx_events_created_at ON events (created_at DESC);

🔄 Request Lifecycle

1. HTTP Request Flow

2. Survey Response Processing

🚀 Performance Architecture

Caching Strategy

Cache Implementation

from functools import wraps
import redis
import json
from typing import Optional, Any

class CacheService:
    def __init__(self, redis_client: redis.Redis):
        self.redis = redis_client
        self.local_cache = {}  # L1 cache
        
    async def get(self, key: str) -> Optional[Any]:
        # L1 cache check
        if key in self.local_cache:
            return self.local_cache[key]
            
        # L2 cache check (Redis)
        data = await self.redis.get(key)
        if data:
            decoded = json.loads(data)
            self.local_cache[key] = decoded  # Store in L1
            return decoded
            
        return None
    
    async def set(self, key: str, value: Any, ttl: int = 300):
        # Store in both caches
        self.local_cache[key] = value
        await self.redis.setex(key, ttl, json.dumps(value))

# Cache decorator
def cached(ttl: int = 300, key_prefix: str = ""):
    def decorator(func):
        @wraps(func)
        async def wrapper(*args, **kwargs):
            # Generate cache key
            cache_key = f"{key_prefix}:{func.__name__}:{hash(str(args) + str(kwargs))}"
            
            # Try cache first
            cached_result = await cache_service.get(cache_key)
            if cached_result is not None:
                return cached_result
            
            # Execute function and cache result
            result = await func(*args, **kwargs)
            await cache_service.set(cache_key, result, ttl)
            return result
        return wrapper
    return decorator

# Usage example
@cached(ttl=600, key_prefix="survey")
async def get_survey_definition(survey_id: str) -> dict:
    return await survey_repository.get_by_id(survey_id)

Database Optimization

Query Performance

-- Composite indexes for multi-tenant queries
CREATE INDEX CONCURRENTLY idx_responses_tenant_survey_created 
ON responses (tenant_id, survey_id, created_at DESC);

-- Partial indexes for common filters
CREATE INDEX CONCURRENTLY idx_responses_completed 
ON responses (tenant_id, survey_id, completed_at) 
WHERE status = 'completed';

-- Covering indexes for read-heavy queries
CREATE INDEX CONCURRENTLY idx_surveys_list_covering
ON surveys (tenant_id, created_at DESC) 
INCLUDE (title, status, version);

-- Full-text search indexes
CREATE INDEX CONCURRENTLY idx_responses_data_gin
ON responses USING GIN ((data::text) gin_trgm_ops);

Connection Pooling

from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker

# Production-optimized engine configuration
engine = create_async_engine(
    database_url,
    # Connection pool settings
    pool_size=20,              # Base connections
    max_overflow=30,           # Additional connections under load
    pool_pre_ping=True,        # Validate connections before use
    pool_recycle=3600,         # Recycle connections every hour
    
    # Query optimization
    echo=False,                # Disable SQL logging in production
    future=True,               # Use SQLAlchemy 2.0 API
    
    # Connection string parameters
    connect_args={
        "server_settings": {
            "application_name": "cimigo_collect_api",
            "jit": "off",      # Disable JIT for consistent performance
        }
    }
)

AsyncSessionLocal = sessionmaker(
    engine, 
    class_=AsyncSession, 
    expire_on_commit=False
)

🔐 Security Architecture

Authentication & Authorization Flow

Multi-Tenant Security

from functools import wraps
from fastapi import Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession

class TenantContext:
    def __init__(self, tenant_id: str, user_id: str, permissions: List[str]):
        self.tenant_id = tenant_id
        self.user_id = user_id
        self.permissions = permissions

async def get_current_tenant_context(
    token: str = Depends(oauth2_scheme),
    db: AsyncSession = Depends(get_db)
) -> TenantContext:
    # Validate JWT token
    payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
    
    # Extract tenant and user information
    tenant_id = payload.get("tenant_id")
    user_id = payload.get("sub")
    
    if not tenant_id or not user_id:
        raise HTTPException(
            status_code=status.HTTP_401_UNAUTHORIZED,
            detail="Invalid token"
        )
    
    # Get user permissions for this tenant
    permissions = await get_user_permissions(db, user_id, tenant_id)
    
    # Set tenant context for RLS
    await db.execute(text(f"SET app.current_tenant_id = '{tenant_id}'"))
    
    return TenantContext(tenant_id, user_id, permissions)

def require_permission(permission: str):
    def decorator(func):
        @wraps(func)
        async def wrapper(
            context: TenantContext = Depends(get_current_tenant_context),
            *args, **kwargs
        ):
            if permission not in context.permissions:
                raise HTTPException(
                    status_code=status.HTTP_403_FORBIDDEN,
                    detail=f"Permission '{permission}' required"
                )
            return await func(context=context, *args, **kwargs)
        return wrapper
    return decorator

# Usage in routes
@router.get("/surveys")
@require_permission("surveys:read")
async def list_surveys(
    context: TenantContext = Depends(get_current_tenant_context),
    db: AsyncSession = Depends(get_db)
):
    # RLS automatically filters by tenant_id
    surveys = await survey_service.list_surveys(db, context.tenant_id)
    return surveys

Data Encryption

from cryptography.fernet import Fernet
import os

class EncryptionService:
    def __init__(self):
        # Use different keys for different data types
        self.pii_key = Fernet(os.environ["PII_ENCRYPTION_KEY"])
        self.response_key = Fernet(os.environ["RESPONSE_ENCRYPTION_KEY"])
    
    def encrypt_pii(self, data: str) -> str:
        """Encrypt personally identifiable information"""
        return self.pii_key.encrypt(data.encode()).decode()
    
    def decrypt_pii(self, encrypted_data: str) -> str:
        """Decrypt personally identifiable information"""
        return self.pii_key.decrypt(encrypted_data.encode()).decode()
    
    def encrypt_response_data(self, data: dict) -> str:
        """Encrypt sensitive response data"""
        json_data = json.dumps(data)
        return self.response_key.encrypt(json_data.encode()).decode()
    
    def decrypt_response_data(self, encrypted_data: str) -> dict:
        """Decrypt sensitive response data"""
        decrypted = self.response_key.decrypt(encrypted_data.encode()).decode()
        return json.loads(decrypted)

# Usage in models
class Response(Base):
    __tablename__ = "responses"
    
    id = Column(UUID, primary_key=True)
    tenant_id = Column(UUID, nullable=False)
    survey_id = Column(UUID, nullable=False)
    
    # Encrypted response data
    _encrypted_data = Column("data", Text)
    
    @property
    def data(self) -> dict:
        if self._encrypted_data:
            return encryption_service.decrypt_response_data(self._encrypted_data)
        return {}
    
    @data.setter
    def data(self, value: dict):
        self._encrypted_data = encryption_service.encrypt_response_data(value)

📡 Event Architecture

Webhook Delivery System

Webhook Implementation

from celery import Celery
import httpx
import json
from datetime import datetime, timedelta

# Celery configuration
celery_app = Celery(
    "webhook_delivery",
    broker="redis://localhost:6379/0",
    backend="redis://localhost:6379/0"
)

@celery_app.task(
    bind=True,
    autoretry_for=(httpx.RequestError, httpx.HTTPStatusError),
    retry_backoff=True,
    retry_backoff_max=600,  # 10 minutes max delay
    max_retries=5
)
async def deliver_webhook(self, webhook_delivery_id: str):
    """Deliver webhook with exponential backoff retry"""
    
    # Get webhook delivery details
    delivery = await get_webhook_delivery(webhook_delivery_id)
    
    if not delivery:
        logger.error(f"Webhook delivery {webhook_delivery_id} not found")
        return
    
    try:
        # Prepare webhook payload
        payload = {
            "event": delivery.event_type,
            "timestamp": delivery.created_at.isoformat(),
            "api_version": "v1",
            "data": delivery.payload,
            "delivery_id": delivery.id
        }
        
        # Sign the payload
        signature = create_webhook_signature(
            payload, 
            delivery.webhook.secret
        )
        
        # Deliver webhook
        async with httpx.AsyncClient(timeout=30.0) as client:
            response = await client.post(
                delivery.webhook.url,
                json=payload,
                headers={
                    "Content-Type": "application/json",
                    "X-Cimigo-Signature": signature,
                    "X-Cimigo-Delivery": delivery.id,
                    "User-Agent": "Cimigo-Collect-Webhook/1.0"
                }
            )
            
            response.raise_for_status()
        
        # Log successful delivery
        await update_delivery_status(
            webhook_delivery_id, 
            "delivered", 
            response.status_code,
            response.headers.get("content-type", ""),
            response.text[:1000]  # First 1000 chars of response
        )
        
        logger.info(f"Webhook {webhook_delivery_id} delivered successfully")
        
    except Exception as exc:
        logger.error(f"Webhook delivery failed: {exc}")
        
        # Update delivery status
        await update_delivery_status(
            webhook_delivery_id,
            "failed",
            getattr(exc.response, 'status_code', None) if hasattr(exc, 'response') else None,
            str(exc)
        )
        
        # Re-raise for Celery retry logic
        raise

def create_webhook_signature(payload: dict, secret: str) -> str:
    """Create HMAC signature for webhook payload"""
    import hmac
    import hashlib
    
    payload_bytes = json.dumps(payload, sort_keys=True).encode()
    signature = hmac.new(
        secret.encode(),
        payload_bytes,
        hashlib.sha256
    ).hexdigest()
    
    return f"sha256={signature}"

# Event dispatcher
class EventDispatcher:
    def __init__(self, db: AsyncSession):
        self.db = db
    
    async def dispatch_event(
        self, 
        tenant_id: str,
        event_type: str, 
        payload: dict
    ):
        """Dispatch event to all registered webhooks"""
        
        # Get active webhooks for this tenant and event type
        webhooks = await self.get_active_webhooks(tenant_id, event_type)
        
        for webhook in webhooks:
            # Create webhook delivery record
            delivery = WebhookDelivery(
                webhook_id=webhook.id,
                event_type=event_type,
                payload=payload,
                status="pending"
            )
            
            self.db.add(delivery)
            await self.db.commit()
            
            # Queue for asynchronous delivery
            deliver_webhook.delay(str(delivery.id))

🔍 Monitoring & Observability

Application Metrics

from prometheus_client import Counter, Histogram, Gauge, generate_latest
import time

# Custom metrics
REQUEST_COUNT = Counter(
    'http_requests_total',
    'Total HTTP requests',
    ['method', 'endpoint', 'status_code', 'tenant_id']
)

REQUEST_DURATION = Histogram(
    'http_request_duration_seconds',
    'HTTP request duration',
    ['method', 'endpoint', 'tenant_id']
)

SURVEY_RESPONSES = Counter(
    'survey_responses_total',
    'Total survey responses submitted',
    ['tenant_id', 'survey_id', 'status']
)

ACTIVE_SURVEYS = Gauge(
    'active_surveys_count',
    'Number of active surveys',
    ['tenant_id']
)

DATABASE_POOL = Gauge(
    'database_connection_pool_size',
    'Database connection pool metrics',
    ['pool_type']
)

# Middleware for automatic metrics collection
@app.middleware("http")
async def metrics_middleware(request: Request, call_next):
    start_time = time.time()
    
    # Extract tenant from JWT token
    tenant_id = extract_tenant_from_request(request)
    
    response = await call_next(request)
    
    # Record metrics
    REQUEST_COUNT.labels(
        method=request.method,
        endpoint=request.url.path,
        status_code=response.status_code,
        tenant_id=tenant_id or "unknown"
    ).inc()
    
    REQUEST_DURATION.labels(
        method=request.method,
        endpoint=request.url.path,
        tenant_id=tenant_id or "unknown"
    ).observe(time.time() - start_time)
    
    return response

# Metrics endpoint
@app.get("/metrics")
async def get_metrics():
    return Response(
        generate_latest(),
        media_type="text/plain"
    )

Structured Logging

import structlog
import logging
from datetime import datetime

# Configure structured logging
structlog.configure(
    processors=[
        structlog.stdlib.filter_by_level,
        structlog.stdlib.add_logger_name,
        structlog.stdlib.add_log_level,
        structlog.stdlib.PositionalArgumentsFormatter(),
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.StackInfoRenderer(),
        structlog.processors.format_exc_info,
        structlog.processors.UnicodeDecoder(),
        structlog.processors.JSONRenderer()
    ],
    context_class=dict,
    logger_factory=structlog.stdlib.LoggerFactory(),
    cache_logger_on_first_use=True,
)

logger = structlog.get_logger()

# Usage in services
class SurveyService:
    async def create_survey(self, survey_data: SurveyCreate, context: TenantContext):
        await logger.info(
            "Survey creation started",
            tenant_id=context.tenant_id,
            user_id=context.user_id,
            survey_title=survey_data.survey.title,
            schema_version=survey_data.schema_version
        )
        
        try:
            survey = await self._create_survey_internal(survey_data, context)
            
            await logger.info(
                "Survey created successfully",
                tenant_id=context.tenant_id,
                survey_id=str(survey.id),
                survey_title=survey.title,
                question_count=len(survey.definition["survey"]["pages"][0]["questions"])
            )
            
            return survey
            
        except Exception as exc:
            await logger.error(
                "Survey creation failed",
                tenant_id=context.tenant_id,
                user_id=context.user_id,
                error=str(exc),
                error_type=type(exc).__name__
            )
            raise

🚀 Scalability Considerations

Horizontal Scaling

# Docker Compose scaling example
version: '3.8'

services:
  api:
    image: cimigo/collect-api:latest
    deploy:
      replicas: 3
      resources:
        limits:
          cpus: '0.5'
          memory: 512M
    environment:
      - DATABASE_URL=postgresql://user:pass@postgres:5432/db
      - REDIS_URL=redis://redis:6379/0
    
  renderer:
    image: cimigo/collect-renderer:latest
    deploy:
      replicas: 2
      resources:
        limits:
          cpus: '0.3'
          memory: 256M
  
  postgres:
    image: postgres:15
    deploy:
      replicas: 1
      resources:
        limits:
          cpus: '1.0'
          memory: 2G
  
  redis:
    image: redis:7-alpine
    deploy:
      replicas: 1
      resources:
        limits:
          cpus: '0.2'
          memory: 256M

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    depends_on:
      - api
      - renderer

Performance Optimization

# Database query optimization
class OptimizedSurveyRepository:
    async def get_survey_with_responses(
        self, 
        survey_id: str, 
        tenant_id: str,
        limit: int = 100,
        offset: int = 0
    ):
        """Optimized query with proper indexing and pagination"""
        
        query = select(Survey).options(
            # Eager load related data to avoid N+1 queries
            selectinload(Survey.responses.and_(
                Response.status == 'completed'
            )).options(
                selectinload(Response.response_data)
            ),
            selectinload(Survey.links)
        ).where(
            and_(
                Survey.id == survey_id,
                Survey.tenant_id == tenant_id,
                Survey.deleted_at.is_(None)
            )
        )
        
        # Add pagination
        query = query.offset(offset).limit(limit)
        
        result = await self.db.execute(query)
        return result.unique().scalar_one_or_none()

# Response caching for expensive operations
@cached(ttl=3600, key_prefix="survey_analytics")
async def get_survey_analytics(survey_id: str, tenant_id: str) -> dict:
    """Cached survey analytics calculation"""
    
    # This is an expensive operation that doesn't need real-time data
    query = text("""
        SELECT 
            COUNT(*) as total_responses,
            COUNT(*) FILTER (WHERE status = 'completed') as completed_responses,
            AVG(EXTRACT(EPOCH FROM (completed_at - started_at))) as avg_completion_time,
            DATE_TRUNC('day', completed_at) as completion_date,
            COUNT(*) as daily_responses
        FROM responses 
        WHERE survey_id = :survey_id AND tenant_id = :tenant_id
        GROUP BY DATE_TRUNC('day', completed_at)
        ORDER BY completion_date DESC
        LIMIT 30
    """)
    
    result = await db.execute(query, {
        "survey_id": survey_id,
        "tenant_id": tenant_id
    })
    
    return {
        "analytics": result.fetchall(),
        "generated_at": datetime.utcnow().isoformat()
    }

---

📚 Next Steps

This architecture overview provides the foundation for understanding the Cimigo Collect Platform. For deeper dives into specific areas:

• Database Design - Detailed schema and optimization patterns (coming soon)
• Multi-tenancy - Complete tenant isolation strategies (coming soon)
• Security Architecture - Comprehensive security implementation (coming soon)
• Performance Optimization - Advanced caching and scaling patterns (coming soon)
• API Development - Building new endpoints and services (coming soon)

The architecture is designed to be scalable, secure, and maintainable while providing excellent developer experience and platform reliability.