Property-level damage evidence
Compare pre-event and post-event imagery, score visible damage, attach evidence, and show confidence at the parcel level for auditable assessment workflows.
GEOSPATIAL PHYSICAL AI · RAY BY RAYFORD AI
The intelligence layer for the physical world.
Ray turns remote sensing, street-level imagery, and geospatial context into auditable intelligence for disaster, infrastructure, and property decisions.
27events
U.S. billion-dollar climate disasters in 2024
$182.7B
Estimated U.S. damage from 2024 disasters
$137B
Global insured catastrophe losses, 2024
5+papers
Published GeoAI research behind Ray's engine
The Ray Platform
Start with disaster. Build toward physical-world intelligence.
Ray is the intelligence system for real-world perception, evidence, and action — starting with the most urgent workflow in property decisions: what changed after a disaster, and what it means.
Claims and inspection triage
Rank properties for adjuster review and package imagery, metadata, and explanations for faster, more defensible insurance claim workflows.
Hazard context and mitigation intelligence
Connect damage evidence with hazard context, mitigation priorities, and resilience planning for properties and critical infrastructure.
Evidence Layer
From geospatial data to review-ready action.
01
Link world context
Parcel records, hazard context, built-environment data, and pre-event imagery assembled for the target area.
02
Compare multimodal evidence
Street-view, satellite, drone, and field imagery across time — fused and aligned at the property level.
03
Arbitrate model signals
Damage scoring, multimodal reasoning, and confidence estimates via Ray's CLIP-enhanced arbitration layer.
04
Export audit trail
Action-ready evidence packages with scores, confidence, and imagery for downstream human review.
Competitive Edge
Not another imagery vendor or pre-event risk score.
Rayford AI focuses on post-event decisions: what changed, how severe it is, what evidence supports the score, and what a human reviewer should inspect next.
Street-view plus remote sensing
Imagery-only tools show pixels. Ray Assess compares ground and overhead evidence at the property level — a dual-sensor approach no pure satellite vendor offers.
Built for disaster response
Pre-event underwriting tools forecast risk. Ray packages post-event damage evidence for recovery decisions — different workflow, different timing, different customer.
Research-backed audit trail
Model arbitration, confidence, and paper-backed methods make output easier for public teams to review, defend, and submit to FEMA or insurers.
Technology Stack
A research-backed geospatial physical AI engine.
Rayford AI translates peer-reviewed GeoAI research into a practical intelligence layer for physical-world perception, evidence, and action.
Initial Wedge
Starting with emergency management, expanding to physical-world intelligence.
2026 PILOTS OPEN
Pilot pricing available.
We are opening a small number of early pilots for historical-event validation and review-ready property assessment workflows.
Discuss a pilot →Emergency management offices
Prioritize preliminary damage assessment when a disaster creates more properties than field teams can inspect. Ray Assess provides ranked, evidence-backed triage.
Local governments, GIS, and infrastructure teams
Convert street-level and aerial data into auditable property and asset layers for assessment, resilience planning, and operational review after major events.
Adjusters and insurance partners
Use the same evidence package for claims triage once the public-sector workflow is validated — shared evidence, faster review, defensible output.
Market Timing
Physical-world risk is rising. Evidence workflows are still slow.
27
U.S. billion-dollar weather and climate disasters in 2024 — the physical world is changing faster than assessment infrastructure can keep pace.
NOAA ↗
$182.7B
Estimated U.S. damage from 2024 billion-dollar disasters — a market where faster evidence means faster recovery and lower total cost.
NOAA ↗
$137B
Global insured natural catastrophe losses reported for 2024 — growing year over year with no slowdown in underlying physical risk.
Swiss Re ↗
Team
Built from GeoAI research for physical-world intelligence.
Yifan Yang
Technical lead for Ray, focused on multimodal spatial intelligence, street-view analysis, model arbitration, and autonomous GeoAI systems.
LinkedIn ↗
Dr. Lei Zou
Advisor for the GeoAI and spatial-intelligence foundation behind Rayford AI's research-to-venture path and disaster resilience direction.
LinkedIn ↗
Dr. Zhengzhong Tu
Committee advisor for computer vision, multimodal model design, and validation strategy for Ray's geospatial physical AI engine.
LinkedIn ↗
Dr. Heng Cai
Committee advisor for built environment context, infrastructure intelligence, and product-risk review for real-world decision workflows.
LinkedIn ↗
Execution Plan · AggieX 2026
From research assets to a pilot-ready geospatial physical AI wedge.
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01In Progress
Complete 40 customer discovery interviews across emergency management, GIS, insurers, adjusters, resilience teams, and recovery consultants.
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02In Progress
Build a working Ray Assess demo for property-level and evidence-driven triage on a historical event.
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03Planned
Create two validation case studies showing before-and-after imagery, evidence, confidence, and workflow relevance.
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04Planned
Secure 3 pilot or letter-of-intent conversations with serious target customers by Demo Day, August 7, 2026.
A technology company for the physical world.
We are scheduling customer discovery and pilot conversations with emergency management, GIS, insurance, resilience, and infrastructure teams.