Case Study: Taranis accelerates GenAI model training with SuperAnnotate

How Taranis accelerated domain-specific GenAI model training with SuperAnnotate

Taranis, a precision agriculture company, needed a better way to turn millions of drone and satellite images into clear agronomic recommendations with generative AI. Its initial approach relied on prompt engineering and spreadsheet-based annotation, but it was too slow, hard to scale, and not accurate enough to meet an urgent planting-season deadline. Taranis partnered with SuperAnnotate to improve its data annotation and model training workflow.

With SuperAnnotate, Taranis used a custom no-code annotation UI, centralized review workflows, and real-time collaboration to speed up iteration and fine-tuning of its specialized AI assistant. The results were strong: model accuracy rose to 95%, annotation review time fell by 80%, the dataset grew 50x, and the team completed the project in just six months, helping Taranis launch on time while saving the equivalent of one full-time role.

Taranis

Gershom Kutliroff

Chief Technology Officer