Descartes Labs is teaching computers how to see the world. Descartes Labs’ team and technology was incubated at Los Alamos National Laboratory with 7 years of research and $15M of funding. Descartes Labs’ first application is to use satellite imagery to gain a better understanding of global crop production.
Challenging forecasting problems: Descartes Labs develops and maintains advanced forecasting solutions, built on Descartes Labs Forecasting Platform (DLFP). While Descartes Labs began by providing packaged solutions for agriculture, DLFP is a general-purpose machine-learning platform well suited for a range of applications.
Descartes Labs’ regular, timely, and accurate forecasts create competitive advantage, especially for organizations involved in trading, logistics, risk management, and security.
Solutions for agriculture: Descartes Labs Crop Forecast is a high-cadence product with global coverage at national, state/province, and more granular resolutions. Our national forecasts support commodity trading and hedging, while the local forecasts support activities across agriculture. We also make weekly forecasts available to the public.
Proprietary forecasting models: In addition to using the agriculture products, organizations can apply DLFP to a range of opportunities and domains.Descartes Labs helps organizations unlock the hidden value of their own data, augmenting it with the data and processing it with machine-learning algorithms.
Customers engage us in a variety of ways. Some simply make use of the data feeds and libraries on a do-it-yourself basis. For others, Descartes Labs’ provides full-service development, including spinning-up secure instances of the platform, on which special purpose applications can be built.
Descartes Labs’ team includes internationally recognized experts in machine learning and large-data computation, with decades of experience working in highly rigorous and secure US national labs. Descartes Labs applies the same scientific rigor and security procedures when building your proprietary forecasting solutions.
Data Wrangling: Descartes Labs resolves inconsistencies between sources and optimize the data’s structure for computational performance. Then we automate these steps, so that the data you generate going forward can be incorporated in real time.
Model Development: Before selecting a production model, Descartes Labs rigorously test hundreds or even thousands of candidate models against the historical data set, iteratively improving the model through machine learning.