Case Study: Cape Analytics achieves 30%+ labeling time savings and faster production AI with Labelbox

How Cape Analytics uses active learning to get to production AI faster

Cape Analytics, a company that extracts property attributes from geospatial imagery to support insurance underwriting, needed to speed up labeling and improve model accuracy for tricky classes like yard debris where taxonomies are hard to define. They turned to Labelbox, using its active learning features and queue management/dynamic queueing to surface low-confidence predictions and prioritize them for review.

Using Labelbox’s active learning workflows and dynamic queueing, Cape Analytics iteratively targeted low-confidence areas, routed tasks to the right labelers, and corrected errors more quickly—resulting in measurable gains: an estimated 30%+ total time savings and avoidance of months of custom engineering, faster fixes, and improved model performance and speed-to-production. Labelbox’s backend and queueing were cited as key differentiators in achieving these outcomes.

Cape Analytics