I analyze how systems behave—human, technical, operational, or AI—and then build the frameworks that make those systems understandable, measurable, and governable. I diagnose ambiguity, drift, and structural failure, finding the underlying patterns that explain why things don’t work the way people think they do. Then I design evaluation systems, KPI architectures, and decision frameworks that convert uncertainty into clarity. My work incorporates analytics,, systems architecture, and data strategy: mapping workflows, revealing hidden logic, and establishing the rules and structures needed for organizations to make consistent, high-integrity decisions at scale. In short, I turn complex, opaque systems into transparent ones—and give leaders the tools to actually run them.

I operate across the disciplines of systems architecture, evaluation science/analysis, and data strategy, using a blend of analytical rigor, conceptual modeling, and pattern-driven reasoning to bring structure to complex environments. My work centers on mapping how systems behave, diagnosing drift and failure modes, and designing KPI and meta-evaluation frameworks that turn ambiguity into measurable, governed logic. I build decision infrastructure that aligns data, processes, and organizational intent, and I design AI evaluation engines and cognitive-system models that clarify how human and technical workflows interact. I work apply ideation, analytical decomposition, continuous learning, and deep-structure thinking to see patterns, causal chains, and conceptual linkages that others miss—then I translate them into operational frameworks that actually work.

I produce evaluative systems and decision frameworks that give organizations structural clarity. This includes meta-KPI architectures, measurement systems, drift-detection engines, and diagnostic models that reveal how processes, teams, and data flows actually behave. I build system maps, requirements structures, and governed decision substrates that translate ambiguity into measurable logic. On the AI side, I develop evaluation engines, instruction architectures, and cognitive workflow models that assess LLM behavior and human–AI interaction. I also generate strategic analyses, cross-domain intelligence assessments, and conceptual models that expose hidden patterns, causal dynamics, and operational risk. The products I create are all designed to make complex systems transparent, measurable, and actionable.

My work is shaped by a fusion of language analysis, process improvement, and advanced quantitative analysis. I began as a writer and editor, developing precision with narrative, structure, meaning, and the mechanics of communication. Later, through intelligence analysis, applied statistics, and systems architecture, I built a complementary discipline rooted in pattern recognition, measurement logic, and quantitative modeling. These two trajectories converge into a single capability: the ability to translate between technical systems, human workflows, and executive intent with clarity. I can map ambiguous problem domains, express them in clean conceptual and mathematical structures, and then articulate those structures in a way that users, engineers, and leadership can all act on. This linguistic–analytical hybrid allows me to bridge the gaps that typically break products and practices — turning technical complexity into human-understandable logic, and turning organizational intuition into governed, measurable systems.

Item descriptionI help leaders interrogate how their systems behave, what their metrics actually signal, and whether the way they govern decisions matches the way work is performed.
The questions include:

• What should we measure, where, and how?
  (What signals matter versus what signals merely exist.)

• Does our reporting tell the truth about how decisions are made?
  (Is the data describing reality, or is it describing aspiration?)

• Do we do what we say we do — and how well do we do it?
  (Execution fidelity and structural honesty.)

• Are our KPIs effective, or performative?
  (Do they change behavior, reveal drift, or simply decorate slides?)

• How do we communicate the ‘so what’ of measurement?
  (Translation from system output to decision intent.)

• How do we leverage AI without outsourcing judgment or creating opaqueness?
  (Evaluation, governance, and fit-for-purpose application.)

• How do we manage these systems over time?
  (Lifecycle management, refresh logic, drift detection, and cognitive hygiene.)

In short — I help organizations understand whether their systems work, why they don’t, and what it will take to run them with integrity.

Because I operate across enterprise, functional, and operational layers, the people I support span multiple altitudes:

• C-suite and enterprise leaders
  who need structural clarity — how the system behaves, where risk sits, and whether decisions align with strategy.

• Senior managers and functional owners
  who require operational coherence — whether processes, governance, and measurement systems actually perform as advertised.

• Mid- and junior-level managers
  who need implementable guidance — how to run the system, adapt it, and maintain integrity.

Resume current as of November 2025.