Bringing cutting-edge data science to emergency response. We connect every call, vehicle and incident into one knowledge graph, then place it in time and space — so the whole response reads at a glance.
Drag the timeline to scrub through the response. Toggle a source in the legend to add or remove it.
Representative incident data — not a real operation. The map is the knowledge graph in space; the timeline is the same events in time, linked by the playhead.
Those gains show when incidents stack up. During declared disasters — like the severe storms that hit NSW from 15 January 2025, when natural-disaster zones were declared across the Hunter and beyond (Maitland, Port Stephens and the Snowy Valleys first, then the Central Coast, Newcastle, Lake Macquarie and Hawkesbury) and crews ran flood rescues, road crashes and structure jobs at the same time — a single shared picture is the difference between agencies working in parallel and working together.
Beyond raw speed: dispatch-to-target adherence held near 94% through the surge, duplicated taskings fell as every controller saw the same units in real time, and the whole response can be replayed in seconds for after-action review — turning each event into training for the next.
Headline figures are representative, benchmarked against published common-operating-picture and GIS studies in emergency management (incident response-time reductions of 15–34%; ~25% faster decision-making). Scenario context: the NSW severe storms from 15 January 2025, with Commonwealth–State natural-disaster zones declared across the Hunter and Greater Sydney (DisasterAssist / NSW Government).
Every call, unit and incident is grounded in the operational record and shown transparently in time and space — a human coordinator stays in command, with the whole response legible at a glance.