Julian Chaoul

Crisis Command is an AI-assisted command center for any emergency: floods, fires, storms or infrastructure failures. In a crisis, responders are buried in 911 calls, photos, sensor readings and radio reports, and they have to decide fast who needs help most and which units can reach them. Crisis Command turns all of that into one ranked list of incidents and a dispatch plan a person approves with one click. We built it with five Meta models. Four run live as agents: Muse Voice Transcribe listens to calls, Muse Spark interprets reports and explains decisions, and SAM 3.1 and Muse Wonder read imagery to spot hazards and close roads that can’t be crossed. The fifth, Muse Image, generated our test imagery. An exact optimizer, not a language model, builds the plan: it routes around closed roads and sends the right teams with enough seats for everyone. When conditions change, everything is re-scored and a new plan is proposed. Nothing is dispatched without human approval, every fact shows its source, and unsupported numbers are flagged. In testing it caught 6 of 6 critical reports and went from report to recommendation in about 20 seconds. Our demo is a simulated flood in Richmond, Virginia, and a new city or crisis type is a configuration change, not new code.