Property-level damage evidence
Compare pre-event and post-event imagery, score visible damage, attach evidence, and show confidence at the parcel level.
Ray by Rayford AI
Every property, ready and recoverable.
Ray turns local hazard history, post-disaster imagery, and property records into auditable damage evidence for insurers, governments, adjusters, and recovery teams.
Ray Assess
Hurricane Milton sample
Damage score
0.78
Confidence
High
Action
Inspect
Roof edge deformation, debris pattern, and street-level evidence indicate moderate structural damage.
First wedge
Damage assessment and claims triage.
Rayford AI starts with one urgent workflow: help human teams decide which properties need attention first, and why.
Claims and inspection triage
Rank properties for adjuster review and package imagery, metadata, and explanations for faster claim workflows.
Resilience intelligence
Expand from post-event assessment into local hazard history, exposure, vulnerability, and practical risk reduction actions.
Beachhead
Built for teams that need property evidence fast.
Insurers and adjusters
Triage claims, prioritize inspections, and reduce uncertainty when disasters generate more properties than field teams can inspect immediately.
Local governments
Convert street-level and aerial data into property-level damage layers for preliminary assessment, recovery planning, and public assistance workflows.
Recovery consultants
Produce faster situational evidence for clients managing resilience projects, recovery funding, and infrastructure repair.
Why now
Disaster losses are rising, but property evidence is still slow.
U.S. billion-dollar weather and climate disasters in 2024.
Estimated U.S. damage from 2024 billion-dollar disasters.
Global insured natural catastrophe losses reported for 2024.
Industry coverage of Yifan Yang and Dr. Lei Zou's Texas A&M Hurricane Milton street-view damage assessment research.
Evidence layer
From imagery to review-ready decisions.
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01
Link property context
Parcel records, local hazard history, and pre-event imagery.
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02
Compare post-event evidence
Street-view, satellite, drone, and field imagery where available.
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03
Arbitrate model signals
Damage scoring, multimodal reasoning, and confidence estimates.
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04
Export audit trail
Property-level evidence packages for human review.
Team
Built from disaster GeoAI research with technical mentorship.
Yifan Yang
Technical lead for Ray, focused on street-view disaster assessment, visual-language models, multimodal arbitration, and autonomous GeoAI.
Dr. Lei Zou
Advisor for the GeoAI and disaster resilience foundation behind Rayford AI's research-to-venture path.
Dr. Zhengzhong Tu and Dr. Heng Cai
Committee advisors supporting model design, validation, built environment context, and product-risk review.
AggieX plan
Ten weeks from research prototype to pilot-ready demo.
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01
Complete 40 customer discovery interviews.
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02
Build a Ray Assess demo for a historical disaster event.
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03
Create two property-level validation case studies.
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04
Secure three serious pilot or LOI conversations.
Rayford AI