AI brings speed. Code review and judgment bring the safety.
I develop with AI support and build AI features into existing applications, under control: with code review, tests, and clear quality gates, instead of shipping AI-generated code into production unchecked.
AI code that hasn't been reviewed and tested is not finished code.
Code generated fast isn't automatically code that carries.
AI now produces in minutes what used to take days. That's exactly the risk: AI code shipped unchecked brings subtle bugs, security holes, and maintenance load that only gets expensive later. Speed without control postpones the problem, it doesn't solve it.
Used responsibly, AI shifts the work to what counts: architecture, review, and the decisions a model can't make.
What I take on.
- building AI features into existing applications, cleanly integrated instead of bolted on
- AI-supported development with mandatory code review and tests before every merge
- architecture and quality gates that secure AI output before it goes live
- judgment for the places where a model is wrong without knowing it
- responsibility for the quality and security of the result, not just for the speed
Speed from the tool. Safety from the process.
AI-generated code doesn't go into production unchecked here. What secures it isn't a single step, but a chain that holds before every merge:
- mandatory code review by a human who checks what the code is supposed to do, not just whether it runs
- automated tests that pin down the expected behavior before AI code changes it
- quality gates before the merge: linting, type checks, and security checks that aren't negotiable
- a deliberate look at where generated solutions typically fail: invented interfaces, unsafe assumptions about data, logic that looks plausible but is wrong
That keeps the speed from the tool, without sacrificing maintainability and security. How I work in general is covered in how I work.
What changes.
- speed from AI without gambling away durability
- AI features that hold in production instead of staying a prototype
- fewer silent bugs and security holes from unchecked code
- a codebase you can keep building on
How controlled AI development works.
How do you use AI in software development under control?
As leverage, not as an end in itself. AI brings speed; the direction comes from judgment and experience. AI-generated code does not go into production unchecked, it runs through code review, tests, and clear quality gates before it goes live.
How is AI-generated code secured?
By the same standard as any other code: mandatory review, automated tests, and quality gates before every merge. AI speeds up the writing, but it does not replace the responsibility for quality and security. That responsibility is exactly what I take on.
Does AI replace development?
No. AI shifts the work to what counts: architecture, review, and the decisions a model cannot make. Especially in the places where a model is wrong without knowing it, you need experience and judgment.
Which AI features can be built into an existing application?
From search through assistance features to processing documents and data, cleanly integrated into the existing product instead of bolted on. Which feature carries and how it can be secured is clarified on the specific product.
Want to bring AI into your application under control?
A first conversation clarifies which AI features carry and how they can be built into your product in a secured way.