An engineering view of how the portfolio website and AI Business Assistant & CRM can be delivered through version control, automated workflows, containerization, and Microsoft Azure.
This page distinguishes the proposed portfolio hosting architecture from the application deployment capabilities developed for the AI platform.
The following diagram represents the intended publishing workflow for this static portfolio website. It is a target architecture and does not, by itself, indicate that Azure hosting or automated deployment is already live.
Git provides a versioned history of source changes. GitHub can serve as the remote repository for portfolio files and deployment configuration.
HTML, CSS, JavaScript, and shared site components are developed and reviewed locally before publication.
A proposed GitHub Actions workflow would validate the website and publish approved changes to the configured hosting target.
Azure Static Web Apps is a suitable hosting option for the portfolio's static frontend. Deployment requires repository integration and hosting configuration.
After deployment, visitors would access the published website over HTTPS. Public availability and deployment status must be verified separately.
Future releases can introduce automated checks, deployment environments, release tracking, and monitoring as operational needs increase.
The AI Business Assistant & CRM has a different deployment profile from the static portfolio website.
Its backend is built with Python and FastAPI, uses PostgreSQL for persistent application data, and integrates with OpenAI and Twilio through external APIs.
Docker and GitHub Actions are part of the application's engineering and delivery work. Microsoft Azure is the intended cloud deployment environment; a live production deployment is not asserted here.
PostgreSQL provides the application data layer, while OpenAI and Twilio remain external service integrations. Their production connectivity, credential management, and operational controls must be addressed as part of deployment.
Source control makes changes traceable and supports identifiable release milestones.
Automated workflows reduce manual deployment steps and help make validation and publishing repeatable.
Local development, testing, and production should use appropriately separated configurations and credentials.
Git history, automated test results, and release documentation help explain how a system changes.
Deployment is not the final step of engineering. It is the beginning of feedback, learning, and evolution.
The deployment roadmap includes publishing the portfolio, preparing application hosting, and strengthening operational practices.
Docker and GitHub Actions are already part of the AI application's engineering work. The roadmap above identifies further deployment and operational capabilities to evaluate or complete.