Clean GIS and natural disaster risk data at property level

We collect and transform heterogeneous data from hundreds of sources, allowing you to understand the real risk of your book of business and price your policies better.

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Satellite Imagery

We collect, classify and analyze satellite images from dozens of sources.


LiDAR technology

We use LiDAR technology to provide precise data for property level accuracy.

machine learning

Machine Learning

We apply Machine Learning algorithms to enhance the data and provide new insights.

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Better data, in real time

Among our ~50 available variables and indexes, we can provide:

Property Data

Building Height

Building Footprints

Building Buffers

Risk Data

Dorothy Flood Risk

FEMA Flood Zones

Big 4 Risks

Other Data



Dorothy Resilience Index

Our technology

Everything you need to know in order to price policies and understand your real risk

LiDAR data allows incredibly accurate measurements

We developed processes to extract information about distances, heights, and surface materials with great accuracy.

Watch and analyze every single footprint

We know how important it is to understand the exact shape of each and every building.

Understand natural risk better

we provide natural disaster risk information utilizing our own proprietary ML models. We've developed models to predict damages more accurately to improve your loss ratio.


Access all the data through our API

All variables are available instantly and securely through our API.

"dem": 5.96725845336914,
"slope": 0.691302299499512,
"flatlow": 0,
"sfha_tf": "F",
"fld_zone": "X",
"landcover": "Developed, Medium Intensity",
"state_name": "FL",
"zone_subty": "MINIMAL FLOOD HAZARD",
"county_fips": 12099,
"county_name": "Palm Beach",
"property_wkt": "MULTIPOLYGON Z (((-8922232.4742 3062456.756 0,-8922246.8344 3062456.756 0,-8922246.8344 3062481.5116 0,-8922232.4742 3062481.5116 0,-8922232.4742 3062456.756 0)))",
"imperviousness": 59,
"potential_fire": "10-20%",
"storm_max_loss": "80000000.0",
"storm_n_tornados": 17,