APPLIED AI
AI for the decisions that increase revenue, drive margin, reduce loss, and cut cost.
Applied AI embedded into operational decision systems on production data platforms, improving measurable economic outcomes.
Shaped by McKinsey problem framing, Microsoft-grade delivery, and private equity value creation discipline.
Start a conversationSome surfaces reveal their truth only end to end.
Some businesses work the same way.
Decision engines that run in production.
From hypothesis to EBITDA. From science to fiscal discipline.
Scope
Define the objective function, constraints, and economic KPI before building anything.
Model SetupValidate
Test falsifiable hypotheses on real data. Separate signal from noise and quantify uncertainty.
EvidenceBuild
Embed the decision into production systems with controls, lineage, and auditability.
ExecutionMeasure
Track realized P&L and EBITDA impact with a value-creation lens and reallocate capital accordingly.
Value RealizationProduction data and AI systems that survive contact with reality.
Correctness under change
Late data, corrections, reprocessing. Atomic writes, explicit merge logic, and reconciliation. Numbers finance can trust.
Lineage and explainability
Metrics trace to source. Ownership is clear. Decisions can be explained after the fact.
Performance and cost behavior
Explicit trade-offs between continuous and on-demand workloads, caching, backfills, and month-end behavior. Predictable performance and cost.
Learning in production
Models are tracked, monitored, and judged by business KPIs. If the metric does not move, the system does not survive.
Methods that compound in production.
AI creates value when it improves a decision that repeats at scale. Not every problem needs deep learning. Often the highest leverage comes from well-chosen statistical and optimization methods embedded into operations.
Operator, not advisor.
Direct engagement with one accountable operator. The problem is framed, the system is built, and the result is measured in production.
I build and operate production AI systems. At McKinsey I learned how to frame complex business decisions. At Microsoft I learned how to ship durable systems. In private equity value creation I learned to focus on measurable financial outcomes. Science and mathematics shape how I test decisions. Michael Lewis and Bill Bryson are favorite writers; Moneyball and A Walk in the Woods are books I return to often. Outside work, I spend my time helping my daughters navigate this world.
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If you're responsible for a decision that materially affects margin, risk, or capital allocation, let's talk.
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