Seed
The AI Range Review is the only fixed fee in the ladder. Keep, fix, or delist: we are paid the same whichever way the grade falls, so the advice is never contingent.
Merchandising-grade AI for retail operators
Every SKU earns its shelf.So should your AI.
You delist lines that do not perform. You mark down stock that does not move. You review the range twice a year without sentiment. We bring the same discipline to your AI portfolio: governed agents on your highest-volume decisions, live in weeks, measured in the numbers you already trade on — margin, waste, basket, labor hours.
Self-funding: nothing from your budget. The AI pays for itself from measured value.
Value and Savings Estimator
See details on bottom line below01The estimate · three levers, four inputs
What would governed agents be worth across your estate?
Store labor is not one-size-fits-all, so the estimate starts with geography. Three levers where supervised agents already earn their keep: price and promo decisions, demand planning against waste and markdowns, and store back-office hours. Defaults are deliberately conservative and every assumption is visible.
1
Store labor is not one-size-fits-all, so the estimate starts with geography. Three levers where supervised agents already earn their keep: price and promo decisions, demand planning against waste and markdowns, and store back-office hours. Defaults are deliberately conservative and every assumption is visible.
02How it is priced · AI always earns itself
Nothing from your budget. Everything from measured value.
The agents pay rent on the shelf. You would never stock a line that ties up cash and hopes to sell through, and the same rule applies here: one small fixed engagement to set the terms, then a deployment that pays its own way.
Earn
Deployment is performance-priced: a small slice of each order, decision, or store-hour the agents actually handle, paid out of margin they create. No license sitting on the P&L waiting to be justified.
Prove
Last season's baseline before go-live, deltas measured weekly, and the meter tied to the numbers you already trade on. If the margin is not there, neither is the fee.
Performance terms are structured per deployment with our delivery partners. The review stays fixed-fee and vendor-neutral precisely so keep-fix-or-delist is never contingent on what gets deployed.
03The plays · where supervised agents earn their keep
Four lines that deserve shelf space in your P&L.
01
Price & promo decisions
Every price move and promo slot rehearsed against margin and elasticity before it hits the shelf edge. Humans set the strategy; agents run the volume.
02
Demand planning
Forecasting that reads weather, events, and the calendar the way your best planner does — on every SKU-store combination, every day.
03
Assortment & space
Range decisions argued with evidence: what earns its facing, what gets cut, what the planogram is leaving on the table.
04
Store back-office
Ordering, counts, gap checks, and reporting drafted by agents and released by people, so hours go back to customers.
04Simulation intelligence · play the season forward
Rehearse the calendar before a single tag changes.
The frontier of retail AI has moved past reporting last season. The shift underway is from backward-looking analytics to simulation intelligence: prices, promotions, and orders rehearsed in synthetic environments before they touch a store — and decided deterministically where money moves.
01
Play the calendar forward, not backward
History tells you how last year's promotions traded. Simulation plays next season forward — prices, promos, and their cross-effects on the basket — before a single shelf-edge label changes.
02
Synthetic baskets, real signal
Generated transaction data that behaves like your customers without being your customers: no loyalty data moved, and rare demand shocks on demand instead of once a decade.
03
Twins for price and stock
A synthetic replica of the category where price moves, range changes, and replenishment rules are tested first. The trading decision arrives with a simulated season attached, not a hunch.
04
Deterministic where it prices
Where logic sets a price, an order, or a markdown: same inputs, same output, every time, fully traceable. Generative AI drafts and describes; it does not set prices.
The rehearsal happens inside the deployment window, in days, not as a research program, and the meter only starts on decisions the rehearsal has already proven. One honesty note: the simulation dividend is already inside levers 1 and 2 above. We deliberately did not count it twice.
05The method · run it like a range review
You review every line twice a year. When did you last review the AI?
01
The Shelf-Space Briefing
45 minutes, free. You leave with the one workflow you would deploy first and the numbers it would be measured on, written in trading language.
02
The AI Range Review
Fixed fee, four to six weeks, vendor-neutral by contract. Every funded AI initiative graded like a line in the range: keep, fix, or delist. Plus the baseline design and the deployment blueprint.
03
Deploy and Measure
Your first supervised agent live in weeks: one banner, one category, one region, measured against last season's baseline, results on the trading agenda by quarter end.
“Autonomy is not a reward for ambition. It is a consequence of evidence.”
If the Range Review does not find an AI play worth its shelf space, we will say so in writing.
Book the briefing →06Start here
Bring one retail AI play. Leave with a measurable first move.
Tell us where you see the clearest retail opportunity. We will respond with a focused next step, not a generic transformation pitch.
Your current estimator result will be included with this requestNot calculatedEstimate details sent with this request: market, annual revenue, store count, back-office hours, labor rate, and calculated impact.