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The Commerce Event Graph

The Commerce Event Graph is ZipF's core dataset. Every checkout the network touches is written as a structured event. The graph is what you query. The products are how different buyers consume it.

An event is observed economic activity at a counter. ZipF records it with a stable schema: merchant, location, timestamp, basket, SKU, quantity, price, discount, tender class, customer cohort, sentiment, and repeat. Because the company generates the bill, the event is complete even when the customer pays cash.

Events become signals when they are normalized across merchants, POS systems, cities, and time. Signals become forecasts when they imply something about a store, a brand, a listed chain, or a macro series. The path is Event to Signal to Forecast to Decision. Different customers pay at different layers.

What sits on the graph

Point-in-time fields travel with production feeds: event_time, ingest_time, published_time, correction_time. Latency is a SKU: T+1, hourly, 15-minute, 1-minute. Distribution can be open, limited, or exclusive.

How the graph grows

Launch GTM connects Square, Toast, Clover, Petpooja, Razorpay, Restroworks, and GoFrugal. Commerce Brain makes the connect worth it in ten minutes. The ZipF terminal is the flagship upgrade: the complete stream, not a 60-80% integration view. Every channel writes into the same normalizer. The join across them is a product no single POS vendor has from its own data.

Questions

What is the Commerce Event Graph?

ZipF's live demand graph of the physical economy. Every checkout is a structured event; the graph is the queryable join of those events across merchants and cities.

What fields are in a ZipF commerce event?

Merchant, location, time, SKU, quantity, price, discount, basket, tender class, cohort, sentiment, and repeat, with point-in-time stamps on production feeds.

Who buys graph access?

Merchants, brands, advertisers, chains, and capital-markets desks. Price and restriction rise from analytics to alpha.

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