Case Study: Nayya achieves trustworthy AI-driven benefits recommendations with Labelbox

Nayya improves benefits recommendations with Labelbox using a ten-minute survey

Nayya, an AI-first company that helps individuals choose employer benefit plans, faced the challenge of finding faster and cheaper ways to produce high-quality labeled data. This signal was needed for offline model training, live prediction evaluation, and verification by subject matter experts across complex data types. To solve this, they turned to the vendor Labelbox.

Using Labelbox Annotate and Catalog, plus a Python SDK-driven approach, Nayya implemented a solution that gave its actuaries insight into model predictions and allowed for their evaluation. The result was a streamlined workflow that enabled Nayya to better visualize its data, maintain signal quality, save time, and rapidly train and test new models. Labelbox provided the expert-verified signal that makes their high-stakes, AI-powered benefits recommendations trustworthy.

Nayya