Context
The organization wanted to move toward AI-enabled modernization, but the information needed for trustworthy architecture assessment was fragmented across source code and delivery systems.
Challenge
AI could not meaningfully assist architecture and modernization without current access to repositories, documentation, tickets, workflows, and system context.
My role
Create the initial context-ingestion architecture and repository automation needed for future AI-assisted analysis.
Approach
- Created scripts that clone or update organizational repositories to a controlled local environment.
- Covered approximately 500 component repositories within the broader 530-repository estate.
- Began creating ingestion paths for source code, PDFs, images, documents, Jira tickets, Confluence pages, and architecture artifacts.
- Organized context to support codebase analysis, architecture assessment, modernization planning, onboarding, and future AI-enabled SDLC workflows.
- Preserved human review as the authority for correctness and architectural intent.
Outcome
- Created a foundation for AI-assisted architecture assessment and modernization.
- Reduced dependence on disconnected tribal knowledge.
- Connected code and delivery artifacts into a common context model.
- Established a safer and more practical path toward AI-enabled engineering workflows.
This work is positioned as the architecture and implementation foundation for AI-enabled modernization — not a fully deployed autonomous AI platform.
What I learned
- Context ingestion is an architecture capability, not merely prompt engineering.
- Human review remains essential for intent and correctness.