Indigo Ag uses satellite data and machine learning to generate personalized, field-level agronomic recommendations for farmers, including which seed treatments best match a given field's soil conditions, yield potential, and environmental stressors. These recommendations are delivered through agronomists and Indigo's digital platform.

Indigo's acquisition announcement for TellusLabs (December 2018) described the combined platform as capable of providing growers with personalized data and recommendations by understanding each field on its own terms. A contemporaneous Esri/WhereNext feature article confirms that Indigo agronomists draw on a complex array of data points — including planting dates, soil pH, soil types, and climate and weather patterns — to match the right seed treatments to the land being cultivated. A patent filing by Indigo Ag (US Patent Application 20190050948) describes a crop prediction system that uses crop prediction models trained via machine learning on geographic and agronomic data to identify an optimized set of farming operations for a grower. The system appears to augment human agronomists rather than fully automate the recommendation process.