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HagXwon AI Learning Platform - Enterprise Architecture Study

Overview

A comprehensive enterprise architecture study for an AI-powered Korean language learning platform, developed using TOGAF (The Open Group Architecture Framework) methodology. This project demonstrates end-to-end enterprise architecture planning from business case through technical implementation.

Project Components

This architecture study includes five key documents that follow TOGAF's Architecture Development Method (ADM):

Business Case

Establishes the strategic rationale for HagXwon, analyzing market opportunity, competitive landscape, and financial projections for an AI-driven Korean language learning platform.

Key Highlights:

  • Market analysis of Korean language learning demand
  • Competitive positioning against existing solutions
  • Financial modeling and ROI projections
  • Risk assessment and mitigation strategies

View Full Business Case →

Business Proposal

Detailed proposal outlining the vision, objectives, and implementation approach for HagXwon as a modern AI-enhanced learning platform.

Key Highlights:

  • Vision and mission statements
  • Target audience segmentation
  • Value proposition and differentiation
  • Implementation roadmap

View Full Business Proposal →

Conceptual Design

High-level architectural design showing system components, data flows, and integration patterns for the AI learning platform.

Key Highlights:

  • System architecture overview
  • Component interaction diagrams
  • Data flow patterns
  • Technology stack decisions

View Full Conceptual Design →

TOGAF Compliance

Detailed mapping of the HagXwon architecture to TOGAF framework principles, demonstrating adherence to enterprise architecture best practices.

Key Highlights:

  • TOGAF ADM phase mapping
  • Architecture principles alignment
  • Governance framework
  • Compliance verification

View Full TOGAF Compliance →

Project Requirements

Comprehensive requirements specification covering functional, non-functional, and technical requirements for the platform.

Key Highlights:

  • Functional requirements (user management, learning modules, AI features)
  • Non-functional requirements (performance, security, scalability)
  • Technical requirements (infrastructure, integrations, APIs)
  • Acceptance criteria

View Full Project Requirements →

Architecture Highlights

AI-Powered Learning

The platform integrates multiple AI capabilities:

  • Conversational AI: Natural language interaction for immersive practice
  • Speech Recognition: ASR for pronunciation feedback
  • Text-to-Speech: TTS for listening comprehension
  • Adaptive Learning: Personalized curriculum based on learner progress
Enterprise Integration

Designed for scalability and enterprise deployment:

  • Microservices architecture for modularity
  • Cloud-native deployment (AWS/Azure/GCP)
  • API-first design for third-party integrations
  • Multi-tenant support for institutional use
Cultural Authenticity

Emphasis on culturally-grounded learning:

  • Korean cultural context integration
  • Authentic dialogue scenarios
  • Cultural nuance training
  • Native speaker validation

Lessons Learned

TOGAF Application

Applying TOGAF to an AI-focused platform revealed:

  • Need for AI-specific architecture patterns
  • Importance of data governance in ML systems
  • Balancing innovation with enterprise standards
  • Iterative refinement of architecture artifacts
AI Platform Design

Key insights from designing an AI learning platform:

  • User experience must balance AI capabilities with simplicity
  • Cultural sensitivity requires domain expert involvement
  • Scalability planning must account for ML model serving costs
  • Privacy and data protection are critical for educational platforms

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