For enterprise & consulting teams
AI Development Without the Hype
I've spent more than 15 years building production software, including years as a Senior Programmer. I was writing software long before today's AI coding tools existed, which gives me a different perspective on what they can actually do.
AI can dramatically accelerate software development. It can turn ideas into working prototypes in hours, explore approaches quickly, automate repetitive implementation work, and make it possible to test concepts that previously wouldn't have justified the development cost.
But it isn't magic.
AI can make bad architectural decisions. It misunderstands requirements. It duplicates functionality, introduces subtle bugs, and can confidently tell you something works when it doesn't. Left unsupervised, those problems compound quickly.
That's where experience matters.
I use AI as an engineering tool, not as a substitute for engineering judgment. My job is to understand what we're trying to accomplish, choose the right approach, direct the AI effectively, recognize when it's going wrong, and make sure what comes out the other side actually works.
That combination lets me move unusually quickly without treating generated code as inherently trustworthy.
Rapid Prototyping
One of the biggest advantages of modern AI development is the ability to make an idea real very quickly.
Instead of spending weeks discussing what an application might look like, we can often build enough of it to interact with, test assumptions, show stakeholders, and decide whether the idea is worth pursuing.
For consulting and sales teams, that can be especially valuable. A working demonstration is often far more compelling than another slide deck.
AI-Assisted Software Development
AI can also significantly reduce the time required to build real applications, particularly when it is being directed by someone who already understands software architecture, APIs, databases, security, user experience, and production deployment.
The goal isn't to remove the engineer from the process.
The goal is to let an experienced engineer get far more done.
Technical Discovery
Not every problem needs an AI solution, and not every AI idea is technically or economically sensible.
I can help evaluate:
- What can realistically be built
- Where AI provides meaningful value
- Whether existing tools can solve the problem
- What should be custom-built
- What a prototype would require
- How systems should integrate with existing infrastructure
- Where security, reliability, or scalability may become concerns
Sometimes the most valuable technical advice is identifying what shouldn't be built.
The Difference
Prompting an AI model is easy. Shipping reliable software is not.
My background is in actually shipping software.
That means I can use the speed of modern AI development while applying the engineering judgment that comes from spending more than a decade building systems without it.
AI makes me faster.
Experience tells me whether what it built is any good.