For a team building with AI, the code is rarely the constraint.
Pipelines, review, observability, cost controls, the platform work that lets a team ship fast and run reliably: those systems are where teams get stuck. I have spent twenty-five years building them at scale, across three IPOs and eight acquisitions.
The new bottleneck is everything around the code
AI tooling has expanded what a strong engineering team can ship, and that output now has to move through integration, review, observability, and the cost of running it all at speed. Without controls around it, AI spend can climb faster than the value it returns. That machinery is what I've spent my career building and running.
Cost controls and guardrails, before the bill surprises you.
AI spend climbs fast without budgets, monitoring, and fallbacks around it. I put those controls in place: the same operations discipline I've brought to production systems for decades.
Expertise decides how far the tools take you.
Anthropic's research shows effectiveness with AI varies sharply by user expertise, part of why most AI initiatives underdeliver (NTT DATA, 2024). The teams getting real leverage have built the operational expertise to wield the tools.
Custom-fit software is now within reach for teams your size.
Modern tooling has made it far more practical to build for your exact workflow, from internal tools to agents and agentic workflows, even without an enterprise-sized team behind it.
How I work
I start every engagement with the same question: where are your bottlenecks and risks right now, and how can we automate them? Answering it takes an honest look at your data, your processes, and your people. The recommendation I give back reflects what's actually right for your business, including the timing.
When the recommendation is to build, I move quickly and carefully. The goal is software your team runs without me. Documented, integrated with the systems you already use.
What I look at first
You get a written recommendation with clear reasoning, so the work that follows is grounded in what your business actually needs.
What we test before we commit
Small, fast experiments tell you a lot. A short validation on a single workflow shows whether the approach is sound before you commit to a full build. By the time you scale, you've already seen it work on your data.
What you own at the end
What I build comes with documentation a non-engineer on your team can read, a runbook for what's most likely to break, and the source under your account, not mine. The handoff happens during the build, not at the end of it.
Three ways to work together
Most teams start with an Operations Audit. Some already know what they want built and skip ahead. A few want an ongoing partner instead of a project.
Start here if you're not sure
Operations Audit
A grounded read on where modernization is likely to pay off, and where it isn't, before you commit engineering time to it.
See details →Start here if you know what you want to build
Implementation Partnership
Build the solution in phases, so you can see progress, judge the fit, and decide whether to continue at every checkpoint.
See details →Start here if you need an ongoing partner
Fractional Advisory
Embedded technical depth when you need it: architecture review, operational guidance, and a straight answer without a full-time hire.
See details →The background behind the advice
I've spent my career building the systems that let engineering teams ship safely under real pressure, through three IPOs and eight acquisitions, on platforms carrying Bazaarvoice and Amazon-scale traffic. That's a different background than most people advising on AI right now, and it shapes how I think about risk and what "done" actually means.
Not sure where to start?
That's the most common place to start. The first call is where we figure out whether it's the audit, the build, or someone other than me.