In-flight
AI Build Partner
Plan in hand? I'll help you actually implement it.
For: Teams with a plan (yours or mine) who need hands-on technical and product leadership to get from roadmap to production.
What this is
You have a plan — yours, mine, or some hybrid of the two. Now, you need someone who can actually build the thing. Not a vendor pitching you a platform. Not a contractor disappearing into a ticket queue. A product-minded engineer who can write the prompts, wire up the integrations, build the AI evals, and own the whole arc from architecture decisions to production rollout.
I bring product judgment to a technical role most teams hire as pure engineering. That matters because the hard parts of an AI implementation aren’t usually code — they’re questions like what does “working” mean here, what happens when the model is wrong, and how do we know we’re still on track six weeks in. Those questions get answered the same way good products get built: with discipline, not vibes.
How it works
We start with a kickoff and a written architecture doc — what we’re building, what the tradeoffs are, what we’re explicitly choosing not to do. The doc gets updated as decisions evolve; the goal is that anyone on your team can read it next year and understand why we did it this way.
From there, scope and build during engineering sprints. I work alongside your engineers — not in a silo, not in a side branch nobody reviews. Pairing is the default. Code review is the default. Knowledge transfer isn’t a phase tacked onto the end; it’s how the work happens. By the time we ship, your team has the context to own it.
Before launch, we build an evaluation harness. This is the part most teams skip and regret. If you can’t measure whether your AI feature is working, you can’t fix it when it stops— and AI features stop working in subtle ways. We’ll have automated evals on the cases that matter, plus a written rubric for the qualitative calls.
What you walk away with
Working software in production, an architecture your team understands, evals that catch regressions, and the documentation to keep extending it without me. I include a 30-day post-launch support window - after that, your team owns it and maintains it.
If you decide you want ongoing oversight after launch, the AI Overseer retainer picks up where this service leaves off.
What you walk away with
- A working AI integration on a timeline you can plan around.
- Architecture decisions that won't bite you in six months — cost, latency, fallback behavior, security, evals.
- Knowledge transfer baked into the engagement, so your team can own and extend it after I leave.
- Honest tradeoff calls in real time — when to use a model vs. a rule, when to buy vs. build, when to wait.
What’s included
- Hands-on implementation: prompts, integrations, evals, guardrails, tooling
- Architecture and tradeoff decisions documented as we make them
- Pairing with your engineers — knowledge transfer is part of the deliverable
- Pre-launch evaluation harness so you know what "working" actually means
- Handoff documentation and a 30-day post-launch support window
Common questions
- Do I need to do the Starter Pack first?
- No. If you have a plan you're confident in, we can start here. If you're not sure your plan is right, we can do a 1-week scope review before committing to a build — cheaper than discovering halfway in that we're solving the wrong problem.
- Can you work with our existing engineers?
- That's the default. I'd rather pair with your team than work in a silo — knowledge transfer is part of the deal.
- What if the scope changes mid-build?
- It will. We replan together at agreed checkpoints, and evaluate trade-offs together - I strive for no surprises, but engineering projects are inherently unpredictable.
Ready to talk?
The fastest way to start is a 30-minute call to see if there’s a fit. No pitch deck, no pressure, just a conversation.
Scope a build