back_office_ops · saas · workflow
Nayya advances personalized benefits recommendations using Labelbox for AI data labeling and model evaluation
Nayya needed faster and cheaper ways to produce labeled training data for ML model development, live prediction evaluation, and subject matter expert verification, drawing on diverse data sources including insurance plans, pharmaceutical data, geospatial data, and financial wellness insights.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User survey submission
Users take a ten-minute survey and receive a plan recommendation that best suits their specific needs.
Tools used
LabelboxLabelbox AnnotateCatalogPython SDKfeature store
Outcome
Nayya's ML team can now better visualize unstructured data, maintain training data quality, save time, and train and test new models faster through more streamlined labeling and enhanced collaboration, with reproducible MLOps pipelines in place.
Results
Time savedsave time
Grounding & classification
Source type: vendor customer story
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personalizationpredictive analyticsrecommendation systempolicy documenthuman review describednamed customerproduction runtime claimedsource backedtools describedworkflow describedfinancial servicesinsuranceaccuracy improvementthroughput increasetime savedvendor customer storyback office opsai draft human approvalhuman review queue