Descartes Labs launched as a spin-out from Los Alamos National Laboratory in December of 2014. The underlying technology uses computer vision, machine learning, and cloud-based infrastructure to teach computers to see and understand the world around them. Initially, the technology was applied to develop an agricultural model to analyze corn and soy production in the United States. Using imagery from various satellite constellations, weather data, and other datasets, the model accurately predicted yield six months prior to the end of the growing season.
Realizing the potential for the technology applied to use cases around the globe, Descartes Labs turned its focus to developing a cloud-based supercomputing platform for the application of machine intelligence to massive data sets. This platform could be used by anyone to develop intelligent learning models to do science on data, and make that data actionable.
Capitalizing on the confluence of advances in AI and high-performance cloud computing – along with the rapid increase of sensors capturing information all over the globe – Descartes Labs has created an enterprise data refinery that enables org
B2C, B2B
101 to 250
Series B
$58.3M
Growth
2014
Government
Financial Exchanges & Data
Environmental & Facilities Services
Data Processing & Outsourced Services
Broadcasting and Media
Agriculture
N/A
N/A
Analytics
IT and Security
Yes
Active
Machine Learning
Natural Language Processing
N/A
Software
N/A
N/A
Geospatial Data Engineer
Remote, Remote
Applied Scientist - Geospatial
Remote, Remote
Software Engineer
Remote, Remote
Site Reliability Engineer
Remote, Remote
Senior Software Engineer
Remote, Remote
AI/ML Professionals
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