Architecture Designed for Clarity, Safety, and Action We don’t use AI to predict disasters. Instead, we use AI only where it matters: to communicate clearly, calmly, and in a way that fits each person’s role.
Many systems rely too heavily on AI for predictions, which can lead to:
Unclear or untrustworthy risk assessments
Generic or alarming messages
Hard-to-maintain, tangled code
One-size-fits-all communication
Our goal is different: keep the prediction transparent and science-based, and use AI only to help people understand and act.
At the core, our system is built around clear separation:
Controller – coordinates everything, but doesn’t contain business logic.
Branches – independent modules that handle specific risks (floods, wildfires, etc.) using physics and data models.
AI Layer – only used here, to tailor communication based on who you are (citizen, first responder, official).
Contracts – fixed, versioned interfaces that keep everything connected cleanly.
This means you can upgrade models, data sources, or AI tools without rebuilding the system.
This is a demonstration version — lightweight, reproducible, and focused on proving the architecture works.
What’s real and won’t change:
Controller-first orchestration
Immutable contracts
Branch isolation
Role-aware communication design
Graceful fallback if a module fails
What’s simplified for the demo:
Hazard models use placeholders (not real disaster data)
Data sources are stubbed (no live APIs)
AI responses are deterministic (not calling live LLMs)
This lets us show how the system behaves without getting lost in data complexities or API delays.
Run the demo in under a minute:
bash python demo_app.py You’ll see:
Risk scores based on simulated physics
Different messaging for citizens vs. officials
Calm, actionable guidance — no panic, no jargon
Who It’s For Citizens get clear, calm guidance tailored to their location and risk.
Emergency managers receive structured, technical briefings they can act on.
Local authorities see role-specific preparedness steps.
Each message is shaped by the person’s role, not by a generic AI prompt.
This demo runs on Python 3.8+ with no external packages. Everything is built using Python’s standard library, so there's no requirements.txt file.
This isn’t just a demo—it’s a production-ready architecture. When it’s time to go live:
Swap mocked models for real physics engines
Replace stubbed data with live satellite feeds or APIs
Plug in governed LLMs for communication
No redesign needed. The interfaces and orchestration stay the same.
Trustworthy – predictions are based on transparent models, not black-box AI.
Safe – AI is limited to communication, not decision-making.
Scalable – add new risk modules without breaking existing ones.
Human-centered – designed for clarity and action, not complexity.
This demo proves the system’s architecture is sound, safe, and ready for real-world use — even though we’ve kept the data and models simple for now.