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This is for founders who need a senior technical leader but can't justify a full-time CTO. You're in the zero-to-one phase — or the one-to-ten phase — and the technical decisions you make right now will define what's possible later. You need someone who has made these decisions before, lived with the consequences, and knows when to optimize for speed versus durability.

The engagement is ongoing and part-time, typically two to four days per week embedded with your team. That means I'm not parachuting in for a strategy deck and disappearing. I'm in the codebase, in the standups, in the architecture discussions. I'm the person your engineers come to when they're stuck, and the person your board hears from when they want to understand the technical roadmap.

What this looks like in practice: making architecture decisions that balance velocity with long-term maintainability, building your hiring pipeline and conducting technical interviews, standing up the processes and delivery systems that let a small team punch above its weight. It also means evaluating vendors and technology partners, and shaping the technical narrative you bring to investors and board meetings. The goal is to build the engineering foundation so that when you're ready to hire a full-time CTO, the house is in order — or to stay on as that leader if the fit is right.

This is for companies that need real AI integration — not a chatbot bolted onto a landing page, but production-grade systems that deliver measurable value. You've seen what's possible and you want to bring it into your product or your operations, but you need someone who understands both the capabilities and the sharp edges of current models.

Engagements are fixed-scope, typically four to twelve weeks from kickoff to production. We start with architecture and model selection — figuring out which problems actually benefit from AI and which ones don't, choosing the right models and providers for your constraints, and designing systems that degrade gracefully when the AI gets it wrong. From there, we build prototype-to-production pipelines that are robust enough for real users.

The work often involves agent and tool-use patterns — systems where language models orchestrate multi-step workflows, call into your existing APIs, and make decisions within guardrails you define. Equally important is the evaluation and observability layer: how you measure whether the system is actually working, how you catch regressions before your users do, and how you build the feedback loops that let the system improve over time. You walk away with a working system and the knowledge to maintain and extend it.

This is for investors running technical due diligence on a potential deal, for founders evaluating whether to build or buy a critical piece of infrastructure, or for teams that need an outside perspective on an architecture decision they can't easily undo. The common thread is that you need a clear-eyed technical assessment from someone who has no stake in the outcome.

These engagements are measured in days to weeks and result in a single, concrete deliverable. That might be a due diligence report that gives an investor confidence in a target company's engineering organization and technical debt profile. It might be a vendor evaluation that lays out the trade-offs between three competing platforms in terms your team can actually act on. It could be an architecture review that identifies the load-bearing walls in a system before a major refactor, or a hiring strategy that defines the roles and sequence for scaling an engineering team.

The value is in compression: taking a question that would otherwise consume weeks of internal debate and turning it into a structured assessment with clear recommendations. I bring the context of having built and scaled systems across industries, so you don't have to learn the lessons the hard way.