These are the decisions where a consumer brand makes or loses money — a price move, a promo plan, a launch, a competitor's cut. Run each one past a calibrated model of your own market first: thousands of shoppers that behave like your real buyers, tuned until the numbers match reality.
See roughly what you'll sell and make, which ideas quietly lose money, and the few worth testing for real — with an honest range on every answer.
Each decision below can be run against your calibrated market before a dollar is spent or a plan is locked.
Find the price that makes the most profit — and see the volume you'd give up to get there, before you move a shelf tag.
See whether a promo actually pays — or just gives margin away and pulls sales you'd have made anyway forward a few weeks.
Know how much a new variant will sell and what it eats into — even with no sales history yet — and where demand goes if you delist.
Build retailer-specific plans, weigh DTC against retail — and get ahead of shoppers (and their AI assistants) asking "what should I buy?"
Filter by decision type. Each card shows how the answer is grounded: Held-out ✓ means validated on real sales the model never saw; Calibrated means fit to real data; Illustrative means a plausible run not yet checked against real data.
How deep can we go on price before the promo stops paying?
What happens to volume and profit if we raise price 5%?
How much will a new variant sell, and what does it cannibalize?
If the store brand undercuts us, who switches?
How much volume do we lose, and should we follow?
Is this promo incremental, or are we just pulling sales forward?
Where does that demand go — to us or to rivals?
When shoppers ask an AI "what should I buy?", are we the answer?
Two of these run on independent public supermarket datasets and are checked against sales the model never saw — that's the Held-out ✓ proof. The rest are calibrated to real data or, where we say so plainly, illustrative directions we haven't yet validated. We'd rather label the gap than hide it.
A number with no error range is a guess in a nicer font. Ours don't come that way.
Every result comes with an honest confidence range and a note on where it stops holding — the categories, price moves, and conditions the model was never asked to cover.
Raw agent simulation looks believable but can land far from the truth; the calibration layer is what pulls the numbers onto reality, and held-out testing is how we know it worked. Directionally decision-grade, not dollar-exact.
We'll build a calibrated model of your own market, run the decisions above against it, and send you the full reproducible benchmark report — including the honest error ranges and where it stops holding.
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