◆ Engagement
One operator. Three shapes.
Most clients work with me in more than one shape over the course of our relationship. The engagement starts wherever the problem is most acute. It evolves as the company does. The relationship is the asset; the engagement mode is just how it gets billed.
◆ Who I work with
Who I work with.
The companies I do my best work with share a profile. AI-native — meaning LLM systems are core to the product, not a sidecar feature. Funded, usually Seed through Series C. Five to fifty engineers. US or EU based, with a founder or CTO who is technical and engaged. Shipping product, not slideware. At a stage where the AI roadmap matters and the cost of getting it wrong is real.
Ideal fit
- ◆AI-native product
- ◆Seed through Series C
- ◆5–50 engineers
- ◆US or EU based
- ◆Technical founder or CTO
- ◆Shipping production
- ◆$1M–$30M ARR
Not the fit
- ◆Pre-product companies
- ◆AI as a sidecar feature
- ◆Hourly billing expected
- ◆Enterprise procurement cycles
- ◆Fewer than five engineers
- ◆Pre-funding
◆ The three shapes
The same operator. Different shapes of engagement depending on what your company needs right now. Most relationships move through more than one.
Make your engineers ten times more productive.
Most engineering teams use AI tools the way they used Stack Overflow in 2015 — sporadically and shallowly. I work directly with your team to install the workflows and tooling discipline that turn Claude Code and agentic development into compounding leverage. Often this is the first shape an engagement takes.
HANDS-ON WORKSHOPS ◆ WORKFLOW DESIGN ◆ TOOLING SETUP ◆ 90-DAY FOLLOW-THROUGH
Build your AI infrastructure properly. Once.
Once your team is operating well, the next problem is usually the system itself. I design and build production AI pipelines from the ground up — multi-agent orchestration, RAG, evaluation frameworks, MLOps — with the same discipline I used to ship TraceLayer in three weeks. Built to ship, built to scale, built to maintain after I hand it off.
ARCHITECTURE ◆ IMPLEMENTATION ◆ EVAL FRAMEWORKS ◆ HANDOFF TO YOUR TEAM
A senior AI operator inside your team.
When the company is scaling and AI is core to the product, the relationship evolves into embedded leadership. Fractional architect, lead engineer, or technical advisor — inside your Slack, in your standup, owning the AI roadmap. The shape that compounds the most for both sides.
FRACTIONAL ARCHITECT ◆ LEAD ENGINEERING ◆ ADVISORY ◆ ROADMAP OWNERSHIP
◆ How this usually unfolds
Every relationship is different. The pattern, though, is recognisable.
First contact
Usually a founder or CTO reaches out because something specific is breaking. The eval framework does not exist. The team is not shipping fast enough. The agent architecture will not survive next quarter. We have a call. If the fit is real, we scope a first engagement — usually training or a defined build.
First engagement
The first shape is always discrete. A workshop series. A specific system built end-to-end. Something with a clear start and finish. This is where we figure out whether we work well together. Most engagements stop here. That is fine — discrete value, discrete outcome.
What comes next
When the relationship continues, it usually shifts shape. The training engagement reveals an architecture problem. The build engagement reveals the need for ongoing ownership. The second shape gets scoped. Sometimes a third. The companies I have worked with longest have moved through all three shapes — sometimes back and forth between them.
Let's see if there's a fit.
The first call is a fit-finder. No deck, no slides — just a conversation about what you are building and whether the relationship makes sense.
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