Commercial strategy & venture development · CPG + AI product strategy · decision systems

I build usable systems from what is already there

When a problem is messy, different functions are looking at different versions of reality. My job is to turn assumptions, data, economics, constraints, and user needs into a decision or a system that actually works. That happens in commercial strategy and venture development, in CPG pricing and margin, and in AI products that have to hold up outside the demo.

Same brain, different rooms. Ventures deciding what to fund next. Pricing files running on stale assumptions. Agents that work until someone real uses them. Mission-driven teams with a priority and no program under it.

See the current work Bring the messy version

How the work moves

From messy inputs to a decision that holds

1
Messy room. Tools, data, instincts, spreadsheets, half-built rules.
2
Stated problem set aside. Real decision named out loud.
3
Logic already doing the work gets surfaced.
4
Smallest usable system gets built. It survives without me.

If the loop doesn't survive without me, it isn't a system.

Current work

What I am actually doing right now.

Full detail →

Commercial strategy and venture development
Pressure-testing business models, pricing, go-to-market, buyer and funder logic, proof requirements, unit economics, and the go/no-go nobody wants to own.
Kindness for Capital: Strategic Advisor.
AI product strategy and validation
Agent behavior, evals, guardrails and responsible use, deployment economics, usage controls, and where human judgment stays in the loop. The line between a product and an expensive demo.
Includes the AI Literacy Diagnostic.
AI Literacy Diagnostic →
CPG and consumer commercial strategy
Pricing, forecasting, category strategy, margin logic, retail and market signal, promotion logic. The longest-running proof base and still active work.
Where the decision discipline was built.
Mission-driven systems and Squirrel Space
Turning priorities into testable programs, tools, and partnerships. Building sponsorship and economic models. Developing AI-literacy tools through a neurodivergent and community lens.
Squirrel Space: Group Project Director.
AI Literacy Diagnostic →

Proof base

The current work sits on operating proof, mostly from CPG.

Pricing, margin, forecasting, market signal, and scale-up work is where the decision discipline was built. The AI work is newer and does not carry the same years of receipts. Both are on the proof page, labeled honestly.

See the proof →

Proof / 01 · Real system behind the stated problem

The $120K study that was already sitting in the company's own data

Context: A roughly $1B protein processor was quoted a licensed elasticity study to answer a pricing question.
Hidden system: The inputs were already in house. The dependency was on a vendor's method, not on missing data.
What changed: Derived the answer from first principles, landed within 0.05% of the licensed result, and folded it trade spend calculators.
Public result: The model is no longer the artifact. The pricing process it seeded is.

Proof / 02 · Scattered knowledge turned into usable logic

Bacon pricing across a roughly 95M-lb portfolio, run on stale assumptions

Context: Pricing ran on static cost thresholds and lagging forecasts against a belly market that did not care about either.
Hidden system: Buyers, planners, and finance each held a piece of the logic. The market was treated as something to react-to vs for.
What changed: Pulled scattered buyer logic, cost-flow rules, and promo behavior into one inventory-weighted, market-reactive pricing process on a weekly cadence.
Public result: Bacon pricing increased by $0.19/LB Value translated across the portfolio, repeatable inside a loop the team ran long after me.

The pattern underneath

The stated problem is usually not the real problem.

The work transfers because the shape of the failure repeats. Different room, same six moves.

How the work moves →Where this usually breaks →

01Set the stated request down. Name the real decision underneath it.
02Inventory what already exists. Tools, data, reports, workflows, people knowledge, the ugly-but-important spreadsheet.
03Find the logic secretly doing the work. Usually one person's head or one tab nobody opens in front of leadership.
04Name the assumption causing the break. Default user, default buyer, default workflow, default forecast.
05Borrow the method from wherever this shape of problem has already been solved. Not best practice. Transferred logic.
06Build the smallest usable system that survives without me.

Writing

The same engine runs in public.

An oil thesis that called $100 Brent ahead of consensus. A beef piece that named producer-level incentive failure before the trade press. A commodity analogy drawn from astrophysics. Same move every time: read the signal in public data that other people were already looking at.

Read the writing →Substack

Start here

Bring the ugly spreadsheet.

The pretty one is probably hiding the problem. Bring the half-built model, the contradictory dashboard, the AI pilot that kind of works, the slide nobody wants to present. That is the raw material.

Start with the messy version →