Where retail brands have shops. Where product brands sell. One map.
Two different questions, kept apart: a retail brand's own outlets, and the shops where someone else stocks a product brand. Both mapped onto the same regions as harmonised public statistics for 195+ countries — so you can read a market instead of spending a quarter assembling one.
Built for analysts, AI builders, and commercial teams
Four steps between a public statistic and your next decision
Three of them have already run by the time you arrive. The fourth is yours.
Collect the public data
Official statistics — population, income, households, business demography — from the national and supranational offices that publish them, harmonised onto one schema and one set of regions. What a market is made of, before anyone has sold anything in it.
Map where the selling happens
Then the points of sale: the outlets a retail brand runs itself, and the shops where someone else stocks a product brand. We keep the two apart — a chain's own store count and a make's shelf presence are not the same fact.
Let AI sort it into markets
Every brand and location arrives described in its source's own words. Language models map those onto one taxonomy and group them into a market — bicycles, groceries, sports — so a question about a market resolves to a set of brands and outlets, not a search string.
Add your own sales
Upload what you sell, store by store. It lands on the same regions and the same grid as everything above, so the map shows where you are strong, where you are absent, and where the demand around you says you should not be.
Your dataThree products, one on top of the next
The data, then the retail layer on the same geography, then one market in depth. Each is a working surface, not a waiting list.
Public data browser
The foundation. Official statistics from many public sources, harmonised onto one schema — consistent column names, standardised geo identifiers, aligned time periods — browsable by region and theme, each carrying its source's own publication date.
Browse the catalog →Brand Locator
Where a brand has its shops, and where its products are sold by someone else — two different questions, kept apart. Mapped onto the same regions as the statistics, so retail reality and public data line up without a join of your own.
See the location data →Geo Sales
One market, in depth. Every outlet binned by hexagonal cell or administrative region, with the share each brand holds in each bin — so you can see where a make is strong, where it is absent, and where the room is.
See how it works →One more way in, not yet open
We would rather tell you it is coming than pretend it has shipped.
Ask the data directly Soon
Put a question to the map in plain language — which provinces stock this make, where a chain has no shop within ten kilometres — and get the answer with the source it came from. An MCP server, so it works inside the AI tools you already use.
Three ways in, each one building on the last
Start free on example markets. Add brand presence across your regions. Then bring your own sales figures.
Free account
A few example markets, open to anyone with an account — real data on the real surfaces, not a demo video. Enough to find out what this gives you for your own market before you commit to anything.
Create an account →Brand browser
Regional insight into brand presence: which brands sit in which regions, at what density, and how that shifts from one province to the next. Where a chain has its shops, and where a make is on somebody else's shelf.
Geo Sales
Geolocated data and the attributes of every point of sale — then your own sales figures on top, store by store. See where you are performing, where you are absent, and where the demand around you says there is room.
Decide where to act
That is what the three are for. Browse the catalog and open the public data browser without talking to anyone; Geo Sales and retail brands come with an onboarding conversation.