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When you look at any store, depot, branch, clinic, or corridor, you see the full real‑world context in one place.
The backbone of the model.
Places
Business
Property
How people and activity flow through space and time.
Mobility
Events
The people and economics behind each area.
People
Economic
Market
The demand and business side.
Retail
Business (plus your own performance data when you choose to bring it)
Each Factori product is one part of the same model:
The Real‑World Graph Model makes complex questions simple to answer, for example:
For each store, branch, or depot: what should we expect next week, given mobility, events, traffic, and local economics?
Which candidate sites truly look like our best performers once we factor in audience, movement, competition, and economics?
Where will demand shift if interest, spend, or movement patterns change in these corridors?
Which neighborhoods and corridors are the best fit for this campaign based on who’s there, where they move, and how they spend?
Which locations need staffing changes next weekend because of events, traffic, or demand shifts?
Where can we safely adjust prices or offers because demand and local conditions support it?
Give our models and copilots one consistent ‘real‑world layer’ so they can answer location‑aware questions out of the box.
The same structure supports forecasting, site selection, marketing, workforce, supply chain, and AI—without re‑designing data from scratch.
You’re not stitching together point solutions; you’re working from one shared representation of the physical world.
The model is built around aggregated, geo‑level signals—never individuals—so it fits enterprise privacy expectations.
As you build more models and assistants, they can all plug into the same, well‑understood “map of reality.”
Teams can ask richer questions earlier, instead of spending months cleaning and reconciling external data.
Start with a region, network, or set of locations you know well.
For example: Places + Mobility + People + Economic around your stores, branches, or depots.
Use the Real‑World Graph Model to answer concrete planning and forecasting questions, and decide where to scale from there.
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