“Rising Waters, Resilient Cities.”

Overview:

Shallow-water and infiltration PDEs embedded in neural networks produce real-time flood inundation maps at 10-, 50-, and 100-year return periods.

Integrates precipitation forecasts, soil moisture profiles (5 cm and 1 m depths), elevation, impervious surfaces, river discharge, and historic flood events.

Delivers flood-depth rasters, expected-loss KPIs (ASCE 24-14, FEMA HAZUS-MH), and geolocated damage categorizations for every structure and critical facility.

Key Benefits:

Mitigate substation and critical-infrastructure flooding: reduce downtime by 40% and avoid $30 M/year in equipment losses.

Inform bank stress-tests and loan underwriting with physics-backed PD/LGD curves, reducing unexpected credit losses by 15%.

Guide city planners to invest $1 in hazard mitigation to save up to $6 in future recovery costs.

“Ignite Insight. Extinguish Risk.”

Overview:

Physics-Informed Neural Networks governed by advection–diffusion–reaction equations simulate fire intensity, ember transport, and heat diffusion.

Real-time and forecast weather, fuel loads, elevation, building footprints, and grid infrastructure feed into city-scale wildfire spread models.

Generates geolocated fire-intensity fields, proximity-based risk scores, and damage categorizations for every home, substation, and transmission corridor.

Key Benefits:

Pinpoint high-risk line segments for targeted vegetation management—reducing ignition events by up to 60%.

Underwrite portfolios with hyper-local fire-exposure metrics, improving combined loss ratios by 5–10%.

Prioritize retrofit and suppression investments: $3 saved in avoided damages for every $1 spent.

“Turning First Principles into Financial KPIs.”

Overview:

Foundational Physics-AI framework that constrains every neural network to the governing equations of fluid flow, heat transfer, and structural mechanics.

Common engine powering all “Ignite,” “Aqualyt,” and future modules—whether modeling hail damage on solar assets or hurricane wind-pressure loads on skyscrapers.

Transparent model architecture ensures that stakeholders can trace every risk score back to real-world PDEs.

Key Benefits:

Build custom “What-If” simulations in days, not months—without specialized HPC clusters.

Seamlessly plug into existing analytics pipelines: export GeoJSON/CSV layers, tabular KPIs, or call via API.

Future-proof your climate-risk strategy: add new hazards (e.g., hail, heat waves) on the same Physics-AI backbone.

“GridGuard™ Physics-AI Twins Fueling Utility Resilience”

Distribution Resilience: Targeted vegetation management to slash wildfire ignitions by 60%.

Transmission Planning: Span-level failure probabilities under 100-year wind or ice loads—avoiding $50M in storm damage.

Substation Flood Protection: Site-specific inundation forecasts reduce downtime by 40% and save $30M/year.

Outage Management: Pre-deploy crews based on real-time hazard overlays, cutting restoration times by 25% and saving $5M per season.

“FinanceGuard™ Turning Climate Uncertainty into Capital Assurance”

Regulatory Stress-Testing: Run SEC/OCC/ECB scenarios in minutes; cut excess capital buffers by 20%.

Precision Underwriting: Asset-level flood and fire intensities improve combined ratios by up to 10%.

Portfolio Resilience: Track mortgages and policies in high-risk zones; adjust reserves proactively, reducing unexpected losses by 15%.

“PropertyShield™ Physics-AI Twins for Resilient Portfolios”

Due-Diligence Risk Assessment: Simulate flood depths and ember-spotting at prospective sites—cut acquisition surprises by $1.2M per high-risk property.

Continuous Monitoring & Reporting: Automate TCFD/SASB/SEC-ready disclosures—halve quarterly update cycles and lower cost of capital by 10 bps.

Retrofit Prioritization: Rank properties by “avoided loss per dollar invested,” boosting ROI on resilience spend by 35% and cutting portfolio-wide expected loss by 40%.