Get more from the team you have.
Consulting for small and mid-sized businesses that want to run leaner. 25 years of engineering leadership across rapid growth, IPOs, and acquisitions.
The cost of picking the wrong problem
AI and modern automation can genuinely change how a business operates, but only when they're the right fit for the right problem at the right time. Getting that answer wrong is expensive. Getting it right means the solution scales with your business instead of sitting on a shelf.
"Where should we actually focus?" takes honest evaluation of your data, your processes, and your specific situation. Not a generic framework applied to every business the same way.
That's the work. Asking the right questions first, so that whatever you build is worth building.
Real leverage requires real foundations.
AI and automation depend on clean data, well-defined processes, and realistic expectations. Getting clear on those foundations before you build is what separates projects that deliver from projects that don't.
Most technology initiatives fail before they deliver.
Research consistently shows that 70–80% of AI initiatives fail to meet their objectives due to organizational readiness (NTT DATA, 2024). The same pattern shows up across most large technology rollouts. Anthropic's research shows effectiveness varies significantly based on user expertise alone, and that gap widens the longer organizations go without guidance.
Context is everything.
What works for a Fortune 500 with a mature data team may not be right for your business. Good guidance starts with understanding your specific situation, not applying a template.
A different kind of consulting
Sometimes the right answer is "not yet." Before you invest in building anything, you should know whether the investment makes sense. That clarity is worth paying for, even when the answer is "wait."
When the answer is yes, I help you validate quickly and build carefully. The goal is working software that delivers ongoing value, not a prototype that demonstrates capability and then sits on a shelf.
Assess first, invest later.
Every engagement starts with an honest evaluation: where will AI help your business, and where won't it? You get a written recommendation with clear reasoning before any development begins.
Validate before you build.
A proof of concept that fails quickly is worth more than a project that fails slowly. I'd rather spend two weeks showing you something doesn't work than two months building something you'll regret.
Build what lasts.
Solutions I build are documented, transfer-ready, and designed to increase in value over time, not to create ongoing dependency on outside help.
Three ways to work together
Most clients start with an Operations Audit. Some already know what they want built. A few are looking for an ongoing partner. All three are the right answer for someone.
Start here if you're not sure
Operations Audit
Fixed price
Find out exactly where to focus, and where not to, before you spend anything on development.
See details →Start here if you know what you want to build
Implementation Partnership
Project-based
Build validated solutions properly: integrated, documented, and with your team trained to use them.
See details →Start here if you need an ongoing partner
Fractional Advisory
Monthly retainer
Embedded technical expertise when you need it: strategic guidance, architecture review, and on-call support without a full-time hire.
See details →The background behind the advice
I've spent my career building things that had to work under real pressure. IPOs. Acquisitions. Massive revenue growth. That's a different background than most AI consultants, and it shapes how I think about risk, timelines, and what "done" actually means.
Not sure where to start?
That's exactly the right question. Let's find out together.