Each item here starts with a concrete business problem, a decision that needed structure, a question standard reporting couldn't answer, or a framework built to give leadership clearer choices. Many are downloadable; all reflect how I actually work.
The problem: finance teams need an integrated budgeting framework that connects assumptions to P&L, tracks actuals monthly, and generates variance analysis, without rebuilding from scratch each cycle.
The problem: revenue grew but gross margin didn't. Leadership needs to know exactly where the margin went, and whether the issue is structural or controllable.
The problem: a business needs a forecast that reflects realistic uncertainty, and leadership needs to know which assumptions actually move the outcome.
The problem: the P&L looks fine, but cash is unpredictable. Leadership needs to see liquidity pressure under different conditions before it arrives.
These walk through a real analytical problem end to end, the question, the method, and the decision it supported. Identifying details of any client work are kept deliberately general.
Showing how pricing discipline and mix optimization delivered 21% higher contribution with fewer units shipped, using elasticity to locate real pricing power.
How a 12-week payment reality check revealed a business running out of cash faster than anyone understood, and what a proper stress test changes.
A worked manufacturing example moving through Reflection, Diagnosis, and Decision Modeling, with a decomposition table and decision model.
A structured benchmark of Claude Opus 4.8 vs Sonnet 4.6 across effort levels on a CFO monthly close task, five conditions, seven scoring dimensions, one clear risk warning.
AI hasn't replaced the analyst, it has removed the ceiling on what one skilled analyst can do. How the ratio of mechanics to thinking has flipped, and what AI still can't do.
The problem: monthly close produces numbers, but assembling a board-ready package, variance, KPIs, narrative, still eats days of analyst time every month.
The problem: pricing, allocation, and capacity decisions involve many variables and constraints at once, usually settled by intuition. A systematic optimization framework can find solutions intuition misses.
The optimization and scenario thinking in my business work is grounded in graduate-level research, including large-scale nonlinear optimization modeling in GAMS for policy scenario analysis. That background is the engine behind the business tools, it isn't a separate practice. The focus of this site is FP&A and financial decision support; the research simply explains the rigour underneath it.
I take on consulting projects, fractional FP&A engagements, financial modeling work, and custom toolkit builds. The more complex the question, the more structured analysis tends to help.