Ray Resilience is an AI-powered WebGIS and smartphone app. Its assistant, Ray, is a risk-analyst agent operating inside a verifiable harness that checks every spatial operation, preserves full provenance, and refuses claims beyond its evidence — with every unknown declared as prominently as every finding. Hazard monitoring is nationwide; exposure and damage analysis exist inside three deep-case areas and the app says so rather than extrapolating.
Each stage consumes and produces auditable artifacts. Nothing is fabricated, nothing is skipped silently — the pipeline either delivers evidence or tells you exactly why it can't.
Every layer in the app traces back to hashed source snapshots through the Steward Harness — CRS asserted, joins verified, uncertainty mandatory, failures preserved in the audit log. Explore all three in the app. (Super Typhoon Bavi 2026, the project's first pre-event capture, is archived append-only and never deleted.)
Disaster tools earn trust by admitting what they don't know. Three rules are enforced in code, not just promised in prose.
Every artifact records its agent, UTC timestamp, and inputs. Pre-event products are frozen — post-event validation can't be quietly rewritten.
No imagery → no damage numbers. Missing inputs → a recorded failure with the reason. The pipeline never interpolates its way past missing evidence.
Every decision product lists what is not known as prominently as what is. An emergency manager deserves the full picture, including the gaps.
The same artifact chain serves three audiences without modification.
Tile-level damage × social-vulnerability priorities with hands-on trade-off sliders, validity badges on every layer, and explicit unknowns. Inspection routes remain future work.
A reproducible, fail-closed pipeline with tests and CI. The frozen pre-event record enables honest "did we predict the harm?" validation — rare in disaster ML.
A hard rule: we point to official warning channels first. Ray Resilience augments official information; it never replaces it.
The test suite doubles as the verifiable evaluation environment: outcome checks, the policy matrix, and pipeline audits all run in CI on every push. Deep-case builders replay against the hashed dataset registry.