A model that keeps learning, not a single tool. Sensors feed a model that learns how people move and decide, and everything it learns flows back into the systems you already run.
A badge or phone signal is pinpointed across the floor. Visitors carry a badge, a clip-on tag, or the event app.
The model reads the whole visit, not one scanpoint: dwell, hesitation, returns, skipped zones, and what led to engagement.
With consent, behavior connects to a real identity, and becomes a ranked lead, a live alert, a pricing input or a recommendation.
Most spaces already carry signal: a badge system, an app, a network. We use what's there and fill the gaps.
Sub-metre precision in live halls today, accurate enough to credit a specific stand. A hall goes live in hours.
Any source can feed the model. Anything the model produces can leave. Online and offline enrich each other instead of sitting in separate silos.
Learns from every visit, enriches every record, predicts the next decision.
Illustrative example, not client data.
A dashboard tells you what happened. The model tells you what to do about it, with the numbers behind it one click away.
Four levels, each with its own legal basis. Identity is never the default. The baseline groups visitors by ticket category and holds no personal records.
The data is yours, and it stays where you want it. We sign a DPA, encrypt what we hold, and run it on the servers you prefer.
The visitor hands back the tag or deactivates the app. Nothing at all is recorded.
Movement with no identity attached. Enough to run a floor: occupancy, flow, zone comparison.
Grouped by ticket type: buyer, press, investor, exhibitor. The group is analysed, never the person, and no personal records exist.
Recommended baseline · legitimate interest · opt-outBehavior linked to a named individual, unlocking lead intelligence and the visitor co-pilot.
Explicit opt-in only · revocableEvents are the densest intent environment on earth, so the fastest place to train. The architecture doesn't care what the building is called.
Happy to walk an engineering or data team through how it's built, what goes in, and what comes out.
