I decide what should be automated, write the specification that makes it buildable, and ship the system that runs it. Most companies buy those three things from three suppliers and pay for the translation. Buy them once.
AI projects don’t fail in the build. They fail in the gap between the strategy deck and the specification, and again between the specification and production. I hold all three so there is no gap to fall into.
Should you automate this at all? Maturity assessment, vendor-neutral build-vs-buy, EU AI Act risk classification, phased roadmap with a business case per phase.
As-is mapping, gap analysis, to-be design, BRDs and FRDs, acceptance criteria, edge-case catalogues, data readiness, UAT coordination.
n8n, Make, Zapier and Power Automate orchestration. React and Next.js portals on Supabase. RAG with Pinecone. MCP integrations. Agents with human review gates.
Every project below follows the same four beats — the problem, what I built, what measurably changed, and the stack it runs on. No jargon required.
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Conventional transformation-consulting discipline applied to AI work — borrowed from a field that learned these lessons two decades ago.
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The stack is selected after the problem is understood, not before. Six capability areas, one operating standard.
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If one of these sounds like your company, you already know what the engagement looks like — and roughly what it costs in time.
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That’s the conversation. One call, no deck — we scope what’s worth automating and what isn’t.
