Assess
GTM AI Readiness Audit
A straight verdict on whether your stack, data, and process can support your AI initiatives, and what to fix first.
GTM Engineering · AI Readiness
Build the AI that's worth building.
Fractional GTM engineering and AI-readiness for B2B marketing and revenue teams. Before you automate anything, you need to know your stack, data, and process can carry it. I can help you find out, then build the parts that compound, and you keep what gets built.
The POV
Most AI projects don't fail on the technology. They fail on the foundation.
Building is the easy part now. Anyone can stand up an agent in an afternoon or build 100 new landing pages. The expensive part is everything underneath it: data clean enough to trust, a process worth automating, and governance that keeps it from going sideways at scale. That's where projects actually break. So the first move is a straight read on whether you're ready, what to fix first, and which builds will compound instead of breaking in six months. You keep everything we build.
Services
Strategy and the build, in one place.
Most teams have to pick between someone who can advise and someone who can ship. I do both: the GTM strategy and the systems underneath it.
- Assess
- Plan
- Govern
- Build
- Own
Assess
MarTech Stack Architecture Assessment
The right stack for the outcomes you're after: keep, consolidate, replace, or buy.
Build
Implementation Retainer
A standing build engine. Work the backlog, ship it instrumented and documented, you own all of it.
Projects
Things I've built.
The fastest way to see how I work is to look at what I've shipped on my own.
TrailSpark
AI signal-intelligence platform that builds buying-group intelligence for PLG. Connects the committee across your systems, then tells you who's missing and how to activate them.
Next Up Poker
Platform for local poker rooms to manage their website and games for members.
Job Scout
A skill that helps automate job search, company research and interview prep.
I brought Ronnie in to make sense of our GTM data and he moved fast. He audited HubSpot and BigQuery and built a knowledgebase my team and our AI agents pull reports from. A week later we were rethinking our signup flow based on what actually converts.
How it works
Diagnose, build, hand off.
Land with a diagnostic, grow into a retainer, expand with projects.
- Diagnose first. A read on what's real, what's ready, and what to fix before a dollar goes into building.
- Build what compounds. Discrete, instrumented, documented. No disposable agents that rot in a quarter.
- Hand it back. Everything ships with the docs and runbooks to run it without me.
Writing
Notes from the build.
Field notes and hot takes on recent trends on AI and GTM.
Is B2B SaaS dead?
AI made building cheap, so the thin stuff might be in trouble. The complex, the regulated, and the things nobody wants to own are not. Most internal builds skip the question that actually matters.
Earn the right to automate
Why most AI projects fail on the foundation, and the calibration that separates AI worth building from AI worth skipping.
About
Twenty years building GTM systems.
Two decades building the systems that connect go-to-market strategy to what actually ships. Lately that means doing it with AI, carefully.
More about meSystems and processes that last
I've designed and implemented multi-million dollar martech stacks and marketing data infrastructure that kept running long after the leadership that scoped it moved on.
Sprawl into something runnable
Turned half-integrated, overlapping stacks into systems a team could actually operate.
Strategic AI implementation
Built AI into live GTM workflows that scaled beyond proof-of-concept.
Signal architecture, end to end
Took PLG and PQL signal models from idea to instrumented pipeline.