Interactive Resume Chatbot Portfolio (WIP)

TOGAF Architecture Development Methodology

This interactive chatbot project leverages the TOGAF Architecture Development Method (ADM) to ensure a robust, scalable, and aligned solution. TOGAF’s structured, Agile-compatible approach provides both clear business alignment and technical flexibility, making it ideal for enterprise-grade solutions.

Architecture Development Cycle (ADC)

TOGAF Architecture Development Cycle Diagram

Figure 1: Architecture Development Cycle (Open Group)

Key Benefits of TOGAF for This Project:

  • Business-Technology Alignment: Ensures all architectural decisions directly support business objectives, making the chatbot an effective tool for dynamic, user-friendly engagement.
  • Scalability with Cloud Infrastructure: TOGAF’s modular approach allows seamless cloud integration, ensuring that the chatbot can scale efficiently as user demands increase or new features are added.
  • Structured, Agile Adaptability: Combines TOGAF’s structured ADM with Agile principles, allowing for rapid iteration and user-driven improvements, ideal for evolving needs.
  • Solution Selection and Risk Management: TOGAF’s solution analysis process supports careful technology selection, minimizing risks and enhancing scalability, security, and compatibility.
  • Enterprise-Grade Reliability and Quality: Emphasizes consistent, reusable components, ensuring the chatbot remains robust, maintainable, and meets professional standards.

Project Phases

Preliminary Phase

Defined scope and principles, focusing on an AI-driven, cloud-scalable solution integrated into the portfolio.

Phase A: Architecture Vision

Visioned an interactive chatbot with enterprise-level design, integrating a Next.js frontend and LangChain backend to create a professional user experience.

Phase B: Business Architecture

Aligned chatbot features with business objectives, focusing on user engagement and enhanced visibility of professional achievements.

Phase C: Information Systems Architecture

Defined data and application architecture, utilizing FAISS for data retrieval and Next.js with Python-based LangChain for interaction handling.

Phase D: Technology Architecture

Established a cloud-based infrastructure with Terraform for scaling, using environment variables for security.

Phase E: Opportunities and Solutions

Identified future expansions such as enhanced NLP and additional data sources to support growth in a modular architecture.

Phase F: Migration Planning

Deployed in phases, leveraging CI/CD via GitHub Actions for continuous updates with minimal downtime.

Phase G: Implementation Governance

Ensured quality through automated testing and monitoring, using tools like CloudWatch for real-time observability.

Phase H: Architecture Change Management

Managed change with version control and iterative updates based on user feedback, aligning with the latest best practices.

Conclusion

TOGAF’s ADM methodology enabled a scalable, enterprise-quality chatbot solution. This project illustrates expertise in adaptive architecture, combining cloud flexibility, structured change management, and robust implementation for a dynamic, user-focused experience.