Case Study: Arturo achieves scalable AI property insights for the $1.2T insurance industry with Labelbox

How Arturo builds AI-first products from the ground up to deliver insights for the $1.2 trillion insurance industry

Arturo, a Chicago-based deep-learning company delivering AI property insights for residential and commercial projects (and serving the $1.2 trillion insurance industry), faced the challenge of building mature, complex AI-first products while controlling resource costs, managing massive labeling workloads, and scaling labeling operations. To address this, Arturo partnered with Labelbox and leveraged Labelbox’s training data platform and the Labelbox‑Python SDK to standardize and accelerate their labeling pipeline.

Labelbox enabled Arturo to operationalize labeling at scale—supporting 5+ million polygons, 100k+ hours of labeling, 60+ instance classes, and a dedicated labeling ops function—so the team could avoid building in-house tools, reduce friction between labeling, ML engineering and operations, and expand AI applications into new territories and use cases. With Labelbox, Arturo accelerated model development, managed growing software and labeling complexity more effectively, and delivered actionable property insights to the insurance market.

Arturo

Gareth Jones

Data Scientist