engagement / consulting
I help teams past the demo stage bring in senior or fractional AI consulting to fix architecture, add release discipline, and make production systems easier to trust and operate.
Updated
Senior AI consulting is most useful when a team has evidence that an AI direction is worth pursuing, but the system is not reliable enough to scale. The work combines architecture, implementation judgment, eval design, release rules, fallback paths, and operating decisions so product and engineering can move with less ambiguity.
This work is for teams that already know the opportunity is real. The problem is that the system is getting harder to trust.
| Track | Best for | Typical deliverables |
|---|---|---|
| AI implementation consulting | Teams moving from demo to production | Routing, eval loops, fallbacks, release gates, runbooks |
| Fractional AI consulting | Teams that need senior direction without a full-time hire | Operating model, standards, prioritization, 90-day mandates |
| Enterprise agent architecture | Companies building complex agent systems across teams | Tool boundaries, permission models, observability, rollout strategy |
Build-side work for teams that need routing, evals, fallbacks, and release discipline in the product itself.
[ See implementation ]Senior coverage for teams that need standards, decisions, and operating pressure without a full-time executive hire.
[ See leadership model ]Bring me in when an AI prototype has become important to the business but the production system still needs clearer architecture, evals, release discipline, fallback behavior, or senior technical ownership. The best fit is a team with real product pressure and decisions that need to connect product goals to engineering reality.
The main tracks are hands-on AI implementation consulting, fractional AI leadership, and enterprise agent architecture. The right track depends on whether the immediate need is building, decision-making, or system design. Implementation work fixes the product system itself. Fractional leadership sets standards, priorities, and decision rhythm. Enterprise agent architecture defines boundaries, permissions, observability, and rollout rules when agent systems span teams or workflows.
The work targets unclear agent boundaries, weak evals, unreliable releases, tool routing mistakes, missing fallback paths, and expensive ambiguity between product and engineering teams. I aim to leave behind fewer hidden failure modes, clearer owners, measurable release gates, and behavior the team can inspect.
The same habits that made Homebrew useful at scale apply to AI systems: practical defaults, understandable behavior, and failure paths people can operate.