Agentic AI. Causal inference. Bayesian methods. We do the math that pricing committees, S&OP, and trade-spend leaders have to defend — faster than your last consultancy thought possible.
Strategy decks don't tell you whether to drop the price 4%. Quarterly dashboards don't tell you which retailer drove the variance. A consultancy readout doesn't quantify what a $2M trade investment actually returns. We do that work — and we defend the math.
Six commercial-decision practices. Each one produces real numbers a real operator has to defend in a real meeting. Elasticity coefficients. Forecast intervals. Causal lift estimates. Optimization frontiers. If we can't put a number on it — and stand behind that number — we don't ship it.
Price elasticity forecasting. Promotion lift quantified causally. Competitive monitoring on autopilot.
Modern marketing mix. Causal lift by channel. Synthetic controls in place of fragile last-touch.
Sell-in and sell-out forecasting, wired into planning. Variance monitored. Demand scenarios simulated.
Probabilistic lifetime value. Churn calibrated to retention spend. Segment unit economics.
Forensic accounting of trade investment. Causal lift by program and retailer.
Earlier detection of velocity shifts, distribution gaps, and execution inconsistencies. Signals before the lagging KPI catches them.
Four methodological commitments. Modern commercial decisions don't yield to A/B tests and dashboard math — they require causal inference, hierarchical priors, and the kind of statistical craft most consultancies have stopped doing. The techniques below are what your last vendor will tell you are "too academic" for your use case. They are why our numbers hold up.
Four phases. It starts with a conversation, not a model. We translate your business expertise into math that actually understands cause and effect — and we stay engaged when the world changes.
We work with you to build a custom, bespoke model of how your business works — not a generic, black-box machine learning system. It starts with a conversation.
We translate your business expertise into mathematical models that actually understand cause and effect. They don't pattern-match. They don't chase correlations. They reason.
Stop building dashboards. Start building decisions. Static narratives become conversational exploration. Manual analysis becomes AI-assisted decision support.
When the world changes, we don't leave you hanging. Models drift. Markets move. Distributions shift. We stay engaged so the math stays operational.
Six operators from FieldGoal, our sibling company — the platform side of commercial execution. Data science, statistics, AI engineering, intelligent design, systems architecture, commercial operations. No analysts in pre-MBA training. No partner-and-pyramid model. Every engagement runs through a senior with their name on the work.
15+ years in commercial systems for field-sales operations. Ex-Rockstar Energy (operations through the $3.85B PepsiCo acquisition). Early career at Raytheon. Founder & CEO of FieldGoal.
Data scientist with 10+ years across statistics and applied software development. Previously at NASA JPL. Leads LLM and agentic AI integration at FieldGoal; brings the same stack to our modeling work.
Quantitative methods and applied statistics. Previously a data scientist at Uber. Causal estimation, experimental design, and the model-to-decision handoff that most analytics work never makes.
10+ years designing AI interfaces for enterprise teams. Builds the layer where analytical output becomes operator action — the difference between a dashboard and a decision.
15+ years architecting production applications. Built FieldGoal's frontend foundation and AI experience layer. Bridges the modeling work and the systems it has to run inside.
Software engineer combining React Native and AI for cross-platform applications. Builds the field execution layer where forecasts and pricing decisions get tested against reality.
Methodology, case studies, and the occasional rant. What most consultancies wouldn't publish — the work, the math, and where the industry is wrong. For readers who want the math.
A F500 beverage operator had spent eighteen months and seven figures on a "pricing transformation" that never shipped a single price change. We rebuilt the model in eight weeks. Here's the math, the methodology, and the receipts.
Last-touch attribution is dead. Multi-touch is a lie. Here's what actually works — and the three causal traps most vendors are still selling around.
Every quarter, the same request: "Can we get a dashboard that shows X?" Every quarter, the answer is the same: no, that's the wrong question. Here's what to ask instead.
When you have three years of data and need to forecast the next launch, frequentist methods fail loudly. Here's the case for hierarchical Bayesian models — and how to defend them in a pricing committee.
A CPG client was over-spending trade by 18 points and couldn't tell where. Our forensic took three weeks. The answer was uncomfortable — and immediately actionable.
Sixty-minute working session with a partner. We'll sketch the analytical approach, the math, and the data we'd need. No deck. No phased engagement plan. If we can't help, we'll tell you who can.
Sixty minutes with a partner. Confidential. No deck required.
A partner will reach out within 24 hours. Confidential.