Industries

Retail

Forecasting, inventory and the customer record all depend on the same thing: data that agrees with itself across every channel. That is usually where the work starts.

Demand and inventory intelligence

A forecast is only as good as the data behind it and the decision in front of it. We build both ends, not only the model in the middle.

01

Demand forecasting

Item-location forecasts accounting for promotion, seasonality and substitution, measured against the planner's current method before anything is replaced.

02

Inventory and allocation

Replenishment and allocation logic tied to the forecast, with safety-stock assumptions made explicit so merchants can argue with them.

03

Pricing and promotion analytics

Elasticity and promotional lift measured on your own transactions, so markdown decisions rest on evidence rather than last year's calendar.

04

Supply chain visibility

Purchase order, shipment and receipt data joined into a single view — often the first time anyone can see where the delay actually sits.

Commerce platforms and the customer record

Omnichannel is a data problem before it is a front-end problem. The customer who returns in store what they bought online has to be the same customer in both systems.

01

Commerce platform modernization

Headless and composable architecture where it earns its complexity, and a straight platform upgrade where it does not.

02

Unified customer data

Identity resolution across channels, with consent and preference held centrally so marketing honors it everywhere rather than per system.

03

Order management

Order orchestration across stores, warehouses and carriers, with availability that reflects reality closely enough to promise against.

Decisions as fast as the channel that feeds them.

Most retail analytics problems are really latency problems. The number is correct; it just arrives after the decision has been made.

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