Indigo Ag uses a machine learning platform called Atlas — built on technology from TellusLabs, which Indigo acquired in 2018 — to analyze satellite imagery, weather data, and historical records in order to forecast crop yields and monitor crop health daily across global farmlands. The system produces field-level predictions and soil maps that agronomists and farmers use to make planting and marketing decisions.

Atlas combines remote sensing, ground equipment data, and weather inputs, applying machine learning models trained on over a decade of historical satellite imagery to predict end-of-season yields and identify differences in crop performance across regions. According to a Geospatial World case study, in 2017 Indigo's models predicted final US corn yield within 1% of the actual figure, five months before the USDA's end-of-season report. The platform also delineates field boundaries from imagery and characterizes local soil conditions. The underlying product originated at TellusLabs, which was acquired by Indigo in December 2018 and integrated as its Geospatial Innovation unit.