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AI-Augmented SDLC
When code becomes a commodity, the bottleneck moves upstream. I've built a complete methodology around that insight, with tooling that bridges taste and judgment on one side and executable code on the other.
Explore →Matthew Bradford
I build things that work, think about why they work, and teach others to tell the difference.
Technologist. Systems thinker. Occasionally blunt about AI, process, and what actually matters.
About
I've been writing software for roughly four decades across seven industries. I've built products, led teams, advised clients, and occasionally had to explain to very smart people why the thing they were building was solving the wrong problem.
I don't stay in one lane — not because I lack discipline, but because the interesting problems don't either. I go wide and deep. The work I'm most proud of lives at intersections: technology and sales, architecture and UX, systems design and legal interpretation. I build things, and the building is usually downstream of synthesis — holding the whole system in view, seeing where the pieces connect, and knowing which pattern is about to become someone's problem before it does. The range is the point.
My goal in any engagement is to leave the people around me more capable than I found them, not more dependent on me. That shows up in how I build, how I lead, and how I teach.
"I am just arrogant enough to know that I can do anything, but wise enough to know that I can't do everything."
Selected Work
02
Automated Assessment Tooling
A fully automated pipeline that takes a structured assessment, runs parallel research, generates a branded narrative report via AI, and delivers it to the recipient's inbox in under five minutes. Zero human intervention.
Explore →03
Study with Heffy
A full AI coaching platform for HESI Med-Surg 2 exam prep. Personalized coach, adaptive weak-area targeting, RN-reviewed content, and a billing model with an auto-stop built in because charging people who aren't using something is wrong.
Explore →The Thinking
AI, when used well, can solve for a lot of the execution. But you still have to know what is worth executing.
That distinction is where the real leverage lives, and it is the thing most organizations are not yet thinking clearly about.
Code is rapidly becoming a commodity. Jevons Paradox tells us what happens next: demand doesn't flatten, it explodes, and the bottleneck moves. It moves to taste. To judgment. To the ability to describe a desired outcome with enough precision that anyone working toward it knows when they've arrived.
There is also a conversation about AI alignment that almost everyone is getting half right. The models need to align to human values. The other direction — humans actively aligning to AI, developing real literacy before the next gate in this transition clears — gets talked about almost not at all.
I've been thinking about all of this for a long time. Here's what I've worked out.
Read the thinkingSpeaking
I speak on AI adoption, methodology, and what organizations actually need to do to get value from these tools. My approach is practical and occasionally blunt. No hype. Frameworks that work in the real world, illustrated with things I've actually built.
Inquire about speakingBeyond the Work
I host karaoke. I build tools out of spite when the software I need doesn't exist. Once I built a nursing exam coaching platform for my daughter. The through line in all of it is the same as the professional work: find the real problem, build the right thing, care about whether it's actually good.