Less “consulting,” more: building durable systems that survive contact with real organizations.
Data modeling, dashboard strategy, reporting design, and adoption—built to answer real questions, not to look pretty.
Designing “if this, then that” systems that remove manual work, reduce errors, and keep teams moving.
Turning AI features into usable workflows—clear inputs, clear outputs, guardrails, and measurable success criteria.
Helping teams plan migrations with less drama: mapping, validation, and “what breaks when this field changes?”
Running workshops, translating between roles, keeping scope sane, and making sure the work actually lands.
Defining success metrics early and tracking them through go-live, so the value is visible and defensible.
I’ve inherited projects that were off the rails—unclear scope, shaky stakeholder trust, and timelines already on fire— and stabilized them by getting alignment fast, clarifying dependencies, and converting “opinions” into testable requirements.
A common pattern: organizations run on spreadsheet reporting because no one trusts the system outputs. My work tends to be: make the data reliable, make the dashboards honest, and make the story clear enough that people actually adopt it.
AI is easy to demo and harder to operationalize. I focus on the surrounding system: inputs, automation steps, exceptions, reporting, and the “what happens when this fails?” plan.
I’ve led implementations that span multiple products with overlapping timelines—workshops, UAT cycles, migrations, and go-live readiness—where the hard part is less “build the thing” and more “keep all the moving pieces from colliding.”
If you want the one-line summary: I like building systems that behave consistently over time—especially when humans are involved.
I’m interested in the gap between “AI can do impressive things” and “AI can be trusted inside an organization.” Most failures aren’t about raw capability. They’re about missing context, brittle processes, unclear ownership, and weak feedback loops.
So I build the scaffolding: automation logic, reporting, governance, and measurable success criteria. It’s the same mindset I bring to robotics—sensors and control are nothing without reliability and a clear definition of “working.”
“What exactly are we trying to improve?” If that’s fuzzy, everything downstream gets weird.
Tools don’t deliver value—systems do. People, process, data, feedback loops, ownership.
I’m not “selling services” here—this page just tells a more complete story. But if you’re working on analytics, automation, or AI adoption and want to compare notes, feel free to reach out.
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