AI-native engineering
AI-native software engineering, built into every stage I ship.
I design, build, test, and deliver software with AI woven into my day-to-day — from research and planning to code review and delivery updates — while ownership, quality, and visibility stay in my hands.
My AI-assisted workflow
- Discovery
- Planning
- Development
- QA & review
- Documentation
- Delivery & visibility
Why it matters
What this means for your project
Faster delivery, same standards
AI speeds up research, boilerplate, and testing — the engineering judgment behind every decision stays mine.
More than just code
Planning, QA, documentation, and delivery visibility are part of how I work — not afterthoughts.
Clear visibility into progress
Regular, honest updates on progress, blockers, and what's next — no surprises at delivery.
AI used responsibly
Every AI-assisted output goes through my own review before it ships. I stay accountable for the outcome.
Realistic estimates
AI-assisted planning helps me scope work accurately before I commit to a timeline.
Quality that holds up
AI-assisted testing and review catch issues earlier, without replacing careful engineering judgment.
Process
How I integrate AI into my day-to-day
Six stages, one workflow — AI supports each of them, judgment drives all of them. Click a stage to see it in practice.
In practice
I'll ask Claude or ChatGPT for a fast first pass on a new API, library, or approach — but I don't take it at face value. Anything I'm going to build on gets checked against the real documentation before I write a line of code.
Tools: Claude, ChatGPT, Perplexity
In practice
I'll paste in a feature description and ask for a task breakdown, then adjust it against what I actually know about the codebase. It's a first draft I edit, not a plan I follow blindly.
Tools: Claude, ChatGPT
In practice
GitHub Copilot handles inline completions while I'm typing. For bigger chunks I describe what I need and review the output line by line before it goes in. I don't ship code I don't understand.
Tools: GitHub Copilot, Claude, Cursor
In practice
I've got AI checks running in my CI pipeline and on every pull request. None of that replaces my own review — I triage every flag myself, since not everything it raises is worth acting on.
Tools: Claude, GitHub Copilot, CI/CD checks
In practice
PR descriptions, changelogs, and README updates start as an AI-generated first pass based on my diff and commit messages. I edit for accuracy, but documentation actually happens instead of being skipped.
Tools: Claude, GitHub Copilot
In practice
When it's time to report progress, I'll turn completed tickets into a clear, non-technical summary with AI, then edit the tone and add context only I have.
Tools: ChatGPT, Claude
Stack
Tools I bring into the workflow
The mix changes per project. Tap a tool to see what it's actually for.
GitHub Copilot
Inline completions while I'm actively coding, plus automated PR checks that flag vulnerabilities, code smells, and improvement suggestions before a human looks at the diff.
Tool choice adapts to each project's stack and constraints.
FAQ
Questions I get about this
Does using AI mean less code review?
No — every AI-assisted change goes through the same review process as anything else I write. AI speeds up the first draft, not the standard.
What if a client doesn't want AI involved?
That's fine — I scope around it. AI is how I work faster, not a requirement for the project.
Do you let AI make architecture decisions?
No. I use it to explore options and stress-test ideas, but architecture and technical direction stay mine.
How do you keep AI-assisted code reliable?
The same way I keep any code reliable: testing, review, and holding it to the same bar regardless of how it was written.
Which AI tools do you actually use day to day?
Mostly GitHub Copilot and Claude for coding, and ChatGPT or Perplexity for research — the mix shifts by project.
Want to build something AI-native?
Let's talk about your project, your stack, and where AI can actually help.
Schedule a call