Four rooms. One discipline.

Problems it addresses

• A business model that has never been pressure-tested against a real buyer or funder.
• Pricing and unit economics that work in the deck and not in the channel.
• A go/no-go decision nobody wants to own because the proof requirements were never written down.

Decisions I help shape

• What has to be true for this to work, and what would prove it false.
• Who actually pays, on what logic, at what margin.
• What gets funded next, and what gets killed early instead of slowly.

Problems it addresses

• An agent that demos well and cannot be trusted on a Tuesday.
• No evaluation set, so nobody can say whether a change made it better or worse.
• Deployment economics discovered after launch, when token spend meets real usage.

Decisions I help shape

• Where human judgment stays in the loop and where it does not.
• What the guardrails, usage controls, and responsible-use boundaries actually are.
• Whether this is a product or an expensive demo, and how you would know.

Problems it addresses

• Pricing running on static cost thresholds against a market that moves weekly.
• Forecasts treated as truth instead of as one input with a known lag.
• Promotion logic and category strategy built on a buyer assumption nobody has re-checked.

Decisions I help shape

• What the price should be this week and what triggers a change.
• Which margin logic holds across the portfolio and which is carrying a stale input.
• What the market signal is saying before the formal system agrees.

Problems it addresses

• Real priorities with no testable program underneath them.
• Partnerships and sponsorships discussed without an economic model.
• AI tools proposed for communities without checking who the default user was.

Decisions I help shape

• Which priority becomes a program, and what test tells you it worked.
• What a sponsorship or partnership has to return to both sides.
• What AI literacy means in practice for people who are not going to read a whitepaper.