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Pain points WHERE COUNTY-LEVEL DATA BREAKS

Insurers run on data that hail doesn't respect.

Hail moves at sub-county scale. Most carriers triage on county-level outlooks, then spend days reconciling claims against a record that was never granular enough to begin with.

What we hear from underwriters & claims
4 ITEMS
County-level outlooks ~2,500 km² of ambiguity Property could be anywhere in the polygon
Claim spikes after every storm Hard to verify No objective record to validate against
Slow triage under CAT load Days to prioritize Adjusters work blind to actual severity
Underwriting data gaps Census + ZIP only No address-level historical exposure
Use cases 4 PRIMARY · CAT-READY

Address-level hail data, four ways.

Same intelligence layer, four entry points across the claims lifecycle. Use one or all four — every signal is available via API, webhook, or the operator console.

Claims triage

Within minutes of a storm, see every monitored address that took hail ≥ a threshold. Route severe-damage claims to senior adjusters; fast-path low-severity. Adjuster utilization improves; CAT cycle time drops.

Fraud detection

Cross-reference reported damage timestamps and sizes against radar-verified hail at the claimed address. Flag claims that don't match the record for second review. Objective evidence instead of "deny and litigate."

Underwriting & risk scoring

5+ years of address-level hail history powers risk models that go beyond ZIP and county. Price exposure correctly; identify portfolios concentrated in high-frequency cells.

CAT response

Real-time impact estimates by policy as the storm moves. Pre-stage adjusters, contact policyholders proactively, route claims to the right teams before the call volume hits.

ROI example SPRING STORM · MIDWEST · 2025

A 400-claim spring storm, triaged before lunch.

A regional carrier covering a four-state Midwest book gets hit by a high-CAPE supercell. Within 2 hours of the last cell exiting the territory, every claim is matched against a radar-verified hail record at the claimed address.

Numbers below are illustrative for a hypothetical 400-claim event. Actual ROI varies with book composition, carrier reserve practices, and event severity.

Outcome readout
4-hr triage
Books triaged in <24h Yes
Adjusters routed by severity 12 senior · 38 junior
Flagged unverifiable claims 120 of 400 (30%)
Estimated avoided indemnity $540K–$960K
<2 hr TRIAGE TIME
30 % FLAGGED RATE
Integration API · WEBHOOKS · BULK

Drops into your stack — not a rip-and-replace.

Connect at whatever layer makes sense. Most carriers start with bulk address sync + CAT webhook, then extend into claims-platform enrichment as their adjusters get used to the data.

Integration surface
REST · JSON · OPENAPI
Claims platforms (roadmap) Salesforce, ServiceNow, Guidewire
Custom systems REST API + webhooks
Bulk geocoding Address book sync
Auth & SSO API keys / SAML SSO (Enterprise)

See it on your book.

A 30-minute walkthrough on a real storm — your geography, your portfolio shape, your claims platform. We'll send a follow-up packet with the data your security team will ask for.

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AI-powered hail intelligence to stay ahead of severe weather — for homeowners, businesses, and anyone who needs to know.

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