On-demand Geospatial Risk Scoring API. We unlock the vast potential of real-world geospatial data, by standardizing and linking hundreds of disparate datasets to deliver trusted, data-based risk intelligence. Our work brings unprecedented transparency to risks that have been traditionally opaque.SaaS API Machine Learning
As we’re living in a world where countries and firms are increasingly interdependent, and which will likely experience instabilities due to global warming, I believe that the need for geospatial risk assessment will increase. And in a market dominated by traditional players, there’s space for better / modern products to serve this need.
Geospatial data analytics is a category of software generally used by big companies (real estate, logistics, manufacturers, telco, agriculture, insurance…), for diverse use cases such as risk assessment, competitive analysis, asset management, sales and marketing optimization… According to Report Buyer the market size is estimated to be $40B in 2018 and is projected to reach $86B by 2023. The growth is mainly driven by the explosion of data available (created by IoT and spatial imagery) combined with advance in AI/ML.
Kernel Risk focuses on the risk management segment and provides a geospatial risk scoring API. They basically extract, aggregate and analyze data that enables the risk scoring (natural disasters, political instability, economic threats…) of specific geographic areas. A typical use case is to model the impact of natural disasters for a manufacturer or an insurance company.
Dataset & Product: Can they provide / extract unique datasets? Can they provide a better risk scoring algorithm than the competition? Can they package it in a developer friendly API?
Go-to-market:which one is more appropriate: a frontal approach as a “replacement tool” to existing solutions? A “sideways” approach by solving new pain points or targeting customers not yet equipped with risk assessment software?