SBIR/STTR Research Award

SBIR Phase II: Combining Physics and AI for Enhanced Risk Assessment

Award identifier ca309484755b4400cdeaffdd30d846963be80b2aa93616386c14a3fc1f95c0caAwarding agency National Science Foundation

Award amount

$1,250,000

Program and phase

SBIR · Phase II

Award year

2026

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
VAYUH INC
Proposal award date
2026-06-17
Contract end date
2028-06-30
UEI
LMHUS3YZNQ93
Solicitation number
NSF 24-580
Topic code
AA

Research abstract

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve the ability to quantify risks significantly impacting global insurance and reinsurance markets. By providing accurate and timely risk prediction information months in advance, the proposed technology will help insurers price more precisely, and reduce coverage gaps. It will also assist governments, energy providers, and supply chain operators in better allocating resources. The technology offers a durable competitive advantage through proprietary modeling pipelines, integration of varied datasets, and a stochastic event generation engine that outperforms historical-data-based models. The business model focuses on a SaaS platform with API-based integration into existing risk tools, supported by tiered subscriptions for insurers and enterprise clients. Integrating AI with atmospheric modeling delivers forward-looking risk assessments that adapt to changing climatological patterns - enhancing safety, and economic resilience. This Small Business Innovation Research (SBIR) Phase II project focuses on the development of a next-generation risk prediction platform that combines physics-based models with deep learning and atmospheric foundation models. The research addresses current limitations in predicting destructive events on sub-seasonal to seasonal timescales, which are critical for insurers and planners. The project aims to refine models that generate high-resolution hazard maps and develop a stochastic event simulator capable of producing synthetic scenarios that reflect future atmospheric conditions. Key innovations include multi-peril modeling architectures, probabilistic assessments, and scalable data infrastructure for real-time access. The project outcomes will improve predictive accuracy, uncertainty quantification, and integration into industry-standard risk tools, setting a new benchmark for weather-driven decision support systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

2 SBIR/STTR records share this UEI (2024–2026). Reported award amounts total $1,525,000. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: National Science Foundation

Agency intelligence

National Science Foundation

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.