forecasting problems in business today
Descartes Labs develops and maintains advanced forecasting solutions, built on the Descartes Platform. While we began by providing packaged solutions for agriculture, Descartes Platform is a general-purpose machine-learning platform well suited for a range of applications.
Our regular, timely, and accurate forecasts create competitive advantage, especially for organizations involved in trading, logistics, risk management, and security.
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 our local forecasts support activities across agriculture. We also make weekly forecasts available to the public.
|Specialty & More|
|Seed & Chemical|
|Ag Coop & Retail|
In addition to using our agriculture products, organizations can apply DLFP to a range of opportunities and domains. We help organizations unlock the hidden value of their own data, augmenting it with our data and processing it with our machine-learning algorithms.
Customers engage us in a variety of ways. Some simply make use of our data feeds and libraries on a do-it-yourself basis. For others, we provide full-service development, including spinning-up secure instances of our platform, on which special purpose applications can be built.
Our 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. We apply the same scientific rigor and security procedures when building your proprietary forecasting solutions.
The heart of our forecasting solutions is a series of models of real-world processes. The models are fed by relevant inputs (e.g., satellite imagery, weather data, etc.); they simulate the dynamics of the underlying process to forecast probable outcomes (e.g., crop yields). We don’t design models directly; we train them using specialized algorithms to correlate historical inputs with historical outputs.
We start by understanding your business needs, context, and the available data; we also help obtain additional data that will improve your model.
We resolve 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.
Before selecting a production model, we rigorously test hundreds or even thousands of candidate models against the historical data set, iteratively improving the model through machine learning.
Proof of Concept
We often start with a sample project to help everyone understand the problem and establish the value of a larger investment. Projects usually incorporate customer data in the modeling process; then candidate models are compared against historical “truth.”
We build models so that you can use them to run your business, every day. Your models and your data live in a private instance of Descartes Labs Forecasting Platform. DLFP ensures security and reliability, and it requires no infrastructure management from you.
By automatically capturing data from your business and other sources, your model continuously improves, becoming a more and more powerful asset for your business.