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Technology Stack Overview

MOOD MNKY combines cutting-edge technologies with robust architecture to deliver a seamless, scalable, and secure platform for personalized fragrance experiences and digital services.

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Architecture Overview

Our technology stack is built around a modern, cloud-native architecture with microservices, serverless functions, and event-driven components.
  • System Architecture
  • Data Flow

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Technology Stack Components

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Frontend Technologies

Web Application

  • Framework: Vue.js 3 with Composition API
  • Build Tool: Vite
  • State Management: Pinia
  • Routing: Vue Router
  • UI Components: Headless UI, Element Plus
  • Styling: Tailwind CSS
  • API Client: Axios, VueUse

Mobile Applications

  • Framework: Flutter
  • State Management: Riverpod
  • Navigation: Go Router
  • API Integration: Dio
  • Local Storage: Hive, SharedPreferences
  • Authentication: Firebase Auth

In-Store Kiosks

  • Framework: Electron
  • Rendering: Vue.js
  • Hardware Integration: WebUSB, WebBluetooth
  • Offline Support: IndexedDB, Service Workers
  • Sync: Custom Replication Protocol

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Backend Services

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Database & Storage

  • Supabase
  • Redis
  • Vector Databases
Our primary database solution, providing:
  • PostgreSQL database
  • Real-time subscriptions
  • Row-level security
  • Authentication services
  • Storage solutions
  • GraphQL interface
  • Vectorization capabilities
For detailed Supabase implementation, see our Supabase Integration documentation.

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AI and Machine Learning

Our AI layer is comprehensive and deeply integrated into the product experience. For detailed implementation, see our AI Integration documentation.

Large Language Models

  • OpenAI GPT-4o: Conversational interfaces, content generation
  • Anthropic Claude 3.5: Complex reasoning tasks, planning
  • Mistral Large: Efficient routine query processing
  • Fine-tuned Models: Domain-specific tasks and data analysis

Machine Learning Models

  • Recommendation Systems: Collaborative and content-based filtering
  • Computer Vision: Product image analysis, visual search
  • Time Series Analysis: Trend forecasting, inventory optimization
  • Clustering: Customer segmentation, fragrance taxonomy

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Infrastructure & DevOps

Multi-cloud architecture leveraging the strengths of different providers:
  • Primary: AWS
    • EC2 for compute
    • S3 for storage
    • RDS for managed databases
    • Lambda for serverless functions
    • CloudFront for CDN
  • Secondary: Vercel
    • Frontend hosting
    • Edge functions
    • Preview deployments
    • Analytics
  • Specialized Services
    • Supabase for database and auth
    • MongoDB Atlas for specific workloads
    • Cloudflare for security and edge compute
Automated development workflow:
  • GitHub Actions: Primary CI/CD platform
  • Testing: Jest, Cypress, Playwright
  • Code Quality: ESLint, Prettier, TypeScript
  • Deployment Strategies:
    • Blue/Green deployments
    • Feature flags
    • Canary releases
  • Monitoring: Datadog, Sentry, LogRocket
Container-based deployment:
  • Docker: Service containerization
  • Kubernetes: Container orchestration (AKS)
  • Helm: Package management
  • Istio: Service mesh
  • ArgoCD: GitOps deployments

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Security Framework

Security is a core concern across our entire technology stack. We implement a defense-in-depth approach with multiple layers of security controls.
1

Authentication & Authorization

  • Multi-factor authentication
  • Role-based access control (RBAC)
  • JWT with short expiration times
  • OAuth 2.0 and OpenID Connect
  • SSO integration
2

Data Protection

  • Data encryption at rest and in transit
  • PII data isolation
  • Regular security audits
  • Compliance with GDPR, CCPA
  • Data minimization principles
3

Infrastructure Security

  • WAF implementation
  • DDoS protection
  • Network segregation
  • Regular vulnerability scanning
  • Penetration testing
4

Application Security

  • OWASP Top 10 mitigations
  • Input validation
  • Output encoding
  • CSRF protection
  • CSP implementation
  • Security-focused code reviews

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Development Workflow

Our development process follows a structured approach:

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Mono Repo Structure

We use a mono repo approach for code organization. For details, see our Mono Repo Structure documentation. 
mood-mnky/
├── apps/                # Application frontends and services
│   ├── web/             # Main web application
│   ├── mobile/          # Mobile applications (Flutter)
│   ├── kiosk/           # In-store kiosk application
│   └── admin/           # Admin dashboard
├── packages/            # Shared packages
│   ├── ui/              # UI component library
│   ├── api-client/      # API client libraries
│   ├── utils/           # Shared utilities
│   └── config/          # Shared configuration
├── services/            # Backend microservices
│   ├── auth/            # Authentication service
│   ├── products/        # Product catalog service
│   ├── orders/          # Order processing service
│   ├── users/           # User management service
│   ├── fragrances/      # Fragrance formulation service
│   └── recommendations/ # Recommendation engine
├── infrastructure/      # Infrastructure as code
│   ├── terraform/       # Terraform configurations
│   ├── kubernetes/      # Kubernetes manifests
│   └── ci/              # CI/CD configurations
├── docs/                # Documentation (this site)
└── tools/               # Development tools and scripts

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Integration Points

Our platform integrates with various external services and APIs:

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Performance Optimization

We prioritize performance across our entire stack with these strategies:

Frontend Optimization

  • Efficient bundle splitting
  • Static asset optimization
  • Route-based code splitting
  • Component lazy loading
  • Image optimization pipeline
  • Critical CSS extraction

API Performance

  • GraphQL for efficient data fetching
  • Multi-level caching strategy
  • Connection pooling
  • Query optimization
  • Rate limiting and backoff
  • Compression middleware

Database Optimization

  • Indexing strategy
  • Query optimization
  • Read replicas
  • Sharding for high-volume tables
  • Database connection pooling
  • Prepared statements

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Monitoring & Observability

  • Metrics
  • Logging
  • Alerting
Key metrics tracked across our platform:
  • Application Performance
    • Response time (p50, p95, p99)
    • Error rates
    • Request volume
    • CPU/Memory utilization
  • Business Metrics
    • Conversion rates
    • Active users
    • Order volume
    • Revenue
    • Product engagement
  • System Metrics
    • Database performance
    • Cache hit rates
    • Queue depths
    • Service health

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Documentation Standards

We maintain comprehensive documentation across our technology stack:
  • API Documentation: OpenAPI/Swagger specs for all services
  • Component Documentation: Storybook for UI components
  • Code Documentation: JSDoc/TSDoc with type definitions
  • Architecture Documentation: C4 model diagrams
  • Runbooks: Step-by-step operational procedures
  • Knowledge Base: Internal wiki for development guidelines

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Future Technology Roadmap

Our technology stack continually evolves to incorporate new technologies and improve existing systems.
  • Q3-Q4 2024
  • Q1-Q2 2025
  • Q3-Q4 2025
  • Migrate to Vue 3 Composition API across all frontend applications
  • Implement GraphQL federation for API unification
  • Enhance AI recommendation engine with new models
  • Expand Supabase utilization for real-time features
  • Implement edge computing for global performance
For more detailed information on specific components of our technology stack, explore the following documentation sections:

Supabase Integration

Learn about our database architecture and Supabase implementation

AI Integration

Explore our AI systems and machine learning capabilities

Mono Repo Structure

Understand our code organization and development workflow