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As published on labelbox.com. Captured by usedby on Oct 7, 2026.

What happened

Nayya, an AI-first company that recommends employer benefit plans, uses Labelbox to produce labeled data for offline model training, live prediction evaluation, and expert verification. Its actuaries use Labelbox's Python SDK to see how the model generates predictions and to evaluate them.

Summary written by usedby from the source page, in English. The figures are those of Labelbox and Nayya, not ours.

Nayya uses Labelbox as a data engine to train models faster through streamlined signal production and collaboration. For never-labeled data, a strong QA process speeds cataloging and extraction from disparate sources.

From the page. labelbox.com, captured Oct 7, 2026

The model-assisted workflow within Labelbox allows us to do this at scale and build a repeatable process for our data scientists as well as any subject matter experts that work with us,

Ishan Babbar, lead data scientist at Nayya, Nayya. Source, captured Oct 7, 2026

What the story claims, and what we checked

We compared the story with its live page on Oct 7, 2026.

  • The passage quoted aboveCheckedCopied word for word from the page, near the name of Nayya.
  • Nayya uses LabelboxCheckedConfirmed line. Latest check across sources: Oct 7, 2026.
  • The result itselfNot checkedWe quote it; we did not measure it.

Same company, same tool or same industry.

8 seconds“with active learning and model-assisted workflows, the average dropped to 8 seconds”Advent Health Partners uses Labelbox. Another customer of Labelbox50%“cut average processing times to create training data by 50%”Blue River Technology uses Labelbox. Another customer of Labelbox70%“70% improvement in time savings for generating insights from images.”Burberry uses Labelbox. Another customer of Labelbox