CAPE Analytics

CAPE is the trusted source for AI-powered property intelligence, providing critical insights into hail risk, roof condition, commercial property risk, and more. #insurtech #underwriting #insurance

CAPE’s Roof Condition Rating (RCR) is approved and actively used by carriers in 41 states. Learn more about our approach to regulatory filing success:?https://lnkd.in/e5Em_wfN #Regulatory #Insurance #Insurtech

CAPE Roof Age is leveraged by top-20 P&C carriers and utilizes aerial imagery and machine learning-powered change detection to produce an accurate roof age for residential and commercial properties across the U.S. Stop by booth 2324 at ITC Vegas to learn about this solution and other CAPE insights: https://lnkd.in/eX6NkwDv #RoofAge #Insurtech #ITCVegas

CAPE's Roof Condition Rating (RCR) has now been approved by state departments of insurance over 250 times! CAPE's dedicated regulatory team has extensive knowledge of individual DOI questions on AI and aerial imagery use, allowing them to advise clients on the most effective approaches for receiving DOI approvals. #Regulatory #Insurtech #Insurance

Stop by our booth at #ITCVegas to learn more about how CAPE’s multisource property analytics are bringing a new level of resilience to the insurance industry. Schedule a meeting or demo with our team here: https://lnkd.in/g7tg9GGf

The CAPE commercial solution includes a growing set of integrated data and analytics, from property condition and construction attributes to weather peril-aligned vulnerability scores and occupancy details. Download our SMB commercial property report to learn how CAPE enables small and midsize property insurers to automate risk selection, boost conversion rates, and increase customer and employee satisfaction: https://lnkd.in/eD3v7FcH

CAPE’s regulatory team focuses on many different aspects of the regulatory environment, from interactions with DOIs to monitoring, internally implementing, and communicating on pending AI regulation across state and federal jurisdictions. In particular, the CAPE regulatory team: ? Supports and responds to both CAPE-led and client-led filing feedback ? Provides guidance and advice on implementing CAPE data into client-led filings ? Interfaces with and educates DOI personnel on AI feature detection technology and its capabilities for the insurance industry ? Provides input on aerial imagery and AI regulation that is under development Learn more about CAPE's approach to regulatory support: https://lnkd.in/e5Em_wfN

CAPE’s commercial property intelligence unlocks the potential to automate underwriting for commercial residential property risks, from small townhouse communities and HOAs to condominium communities and strip malls. In our new report, learn how commercial insurers can streamline underwriting efficiency: https://lnkd.in/gnXWFUfU

A critical factor in assessing a property’s complete hail risk is its roof’s past hail experience. CAPE’s Hail Experience Rating uses historical weather analytics from Canopy Weather, including the number of recent hail events and their maximum size, to estimate the likelihood that a property has experienced recent hail damage. Learn more about CAPE's holistic approach to hail risk in our latest webinar, available on-demand here: https://lnkd.in/eTt9A68N

I was recently asked to evaluate the work of Grande Data Services, Inc., a data-labeling service based in rural El Salvador that reinvests all profits back to the education and career development of the local Chalatenango community. In addition to having a great mission, I'm pleased to report that the quality of their data labels were stellar. I worked with data labeling supervisor Geovany Ramos and founder Joseph Albers to produce a computer vision case-study to test their performance. I also interviewed CAPE Analytics on the value that Grande's services provides for training their computer vision models (Thanks Ryan Scolnik and Renzo Silvera!) If you need better labeled data going into your computer vision or Spanish-language NLP models, take a look! And/or if you happen to be interested in how I fine-tuned GroundingDino to detect a rare image class with 120 labeled images, you can check out my code for this project at https://lnkd.in/gW3SjQJb?