A calibrated twin of your category — watch thousands of agents shop, then try any price before you set it.
Frozen Pizza8,000 simulated shoppers · calibrated on real supermarket data
Try it:demo:
or upload — needs: product, price, units (optional: store, week, market_size, promo)
Prices — drag to run a what-if
vertical mark = actual market share (validation anchor) · NEW = predicted from positioning, no sales history (simulation mode)
Your brand
Your unit cost $
Category share
–
Revenue index
–
Raise your price 10% → who wins the shoppers:
Your price → share & revenue curve
The agent population
Substitution matrix — who wins when a rival raises price
each row = that product raised price +10% · greener cell = the column product captured more of the freed-up share (cross-price substitution)
Compare scenarios (A vs B)
Set prices / assortment / promo, pick your brand, then save the plan — compare two side by side.
Foothold · agent-to-agent deal — real, grounded in the calibration (not a mockup)
A buyer-agent sources the category; each brand's seller-agent offers a price computed from the calibrated model; they negotiate; the winning outcome is booked to the ledger. Every number comes from the demand model.
The outcome ledger — every agent deal, its result & would-be fee
Record-first, monetize-later: we log who brokered what real outcome, and what the fee would be. This ledger — not any single model — is the flywheel and the moat, owned neutrally. It's the spine that makes identity (your agent) + action (agent deals) + intelligence (the calibrated graph) one company.