Challenge
A large-scale engineering team managing dozens of repositories faced mounting technical debt: outdated runtimes, a fragmented CI pipeline spread across multiple platforms, and aging infrastructure-as-code. Modernization work was slow, inconsistent, and consumed senior engineering hours disproportionate to the output.
What We Did
FusionHit integrated AI-assisted development into the team’s full engineering lifecycle. Each repository was configured with structured project context files, eliminating the overhead of re-explaining codebases per session. Specialized agents were set up for repeatable tasks — runtime upgrades, CI pipeline migrations, and cloud infrastructure development — all calibrated to internal engineering standards.
For GitHub, the workflow became fully native: branch creation, PR generation with complete descriptions, CI workflow execution, and check monitoring, all from the terminal. For cloud infrastructure, AI agents interacted directly with CDK configurations, read CloudWatch logs, inspected deployed resources, and surfaced diagnostics without requiring manual log triage.
Results
- Multi-repository .NET runtime upgrades completed in hours, not days
- Full CI pipeline migration from Bitbucket to GitHub Actions executed across multiple repos with consistent quality
- Complex payment tokenization failure diagnosed end-to-end — Lambda logs → database → external gateway — replacing blind debugging with systematic root cause analysis
Why It Matters
In distributed systems, the most expensive engineering work isn’t writing new features — it’s maintaining quality across legacy layers and triaging failures across multiple services. AI as a technical peer compresses both.
