Pick a lens. The world model focuses on a specific slice of your question — route-level traffic, airport ops, crowd dynamics, urban change, or a timeline of what happened. The memory underneath is shared, global, always on.
Traffic Cameras • Route Intelligence
"Reads the traffic slice. Learns a street's rush-hour rhythm and tells you when a commute is about to go bad."
Weather + Cameras • Environmental Learning
"Correlates weather with what the cameras actually see. Catches when heat, humidity, or rain shift behaviour on the ground."
City Cameras • Urban Change Learning
"Watches construction, demolition, land-use shifts. Compares the city to its own baseline and flags what doesn't fit."
Global Cameras • City Pattern Learning
"Compares cities to each other. Finds the ones that rhyme with yours and the ones that don't."
Transit Cameras • Transport Learning
"Watches stations and depots. Learns which services run on time and where the system usually breaks down first."
Street Cameras • Activity Learning
"Reads street-level activity. Knows a plaza's busy hours, the markets' crowd rhythm, and when the neighbourhood is having a quiet night."
All Sources • Historical Memory
"Walks the evidence memory end-to-end. What happened, when, and at what scale — your site's timeline on a single question."
CCTV + Cameras • Real-Time Learning
"Names what it sees in frame. Turns raw camera pixels into the structured events the rest of the memory is built from."
Expanding the world model with new sensor families — drones, LiDAR meshes, underwater arrays. Each one slots into the same memory without a re-train.
Drone feeds joining the same memory soon. No new pipeline, no new dashboard — just more eyes.