Analytics for anywhere people walk through a door
The same counting platform, pointed at six very different problems. Every figure below comes from the same sensors and the same API.
Retail Chains
Staffing rosters and store rankings are usually built on sales alone, which tells you what happened after someone walked in — never how many walked past, or how many came in and bought nothing.
Footfall per store per hour, next to transactions, so conversion stops being a guess. Compare branches fairly by traffic rather than by turnover, and roster staff against the hours that are actually busy.
Shopping Malls
Tenants ask what traffic their unit really gets, and lease negotiations stall on numbers neither side can verify.
Independent counts per entrance and per zone, shared with tenants as reports they can trust. Capacity planning for events, and evidence for rent reviews.
Museums & Exhibitions
Galleries have hard capacity limits and visitor flow that bunches around the popular rooms.
Live occupancy per gallery against capacity, dwell patterns by hour, and a weekday × hour heatmap that shows exactly when to add timed entry.
Airports & Transit
Queues form faster than staff can be moved, and the first sign of trouble is usually a complaint.
Passenger flow at every gate and checkpoint, updating live, so lanes open before a queue becomes an incident rather than after.
Smart Buildings & Offices
Nobody is sure which floors are used, and every square metre is on the lease whether anyone sits there or not.
Occupancy by floor and zone over time, so space decisions rest on measured use rather than on a survey taken one week in March.
System Integrators
You need analytics under your own brand, delivered into systems your clients already run.
A multi-tenant platform with per-client data isolation and an open REST API. Deploy it for your clients, keep your own relationship, integrate it wherever it needs to go.
Which of these is you?
Tell us how many sites you run and what you need to measure. We will show you what the dashboard looks like with your kind of data in it.