We believe soil carbon and soil health to be some of the most important tools in the toolbox to combat climate change and support many of the sustainable development goals. We provide both scale and scientific rigor to the monitoring, reporting, and verification of soil carbon projects.

Our machine learning algorithms utilize public, commercial, and private datasets to create cutting-edge models. The types of data can generally be categorised as satellite imagery, environmental data, and soil carbon samples.

Together with the registries and in close collaboration with the scientific community, we are developing comprehensive, scientifically rigorous methods to measure and verify our models' accuracy - something our nascent industry unfortunately still lacks.

We have hundreds of thousands of data points from our partner network around the world. We also conduct our own targeted sampling campaigns to strategically improve our regional algorithms' performance where necessary.

Our analytics are currently being used in 10 countries around the globe. Our primary focus is in Europe, North America, and Africa. We also have ongoing collaborations in South-East Asia, South America and Australia.