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

Cape Analytics saves 30%+ time with Labelbox

Cape Analytics, a company that provides property data from geospatial imagery to insurers, faced a challenge in improving its AI models. The models were sometimes confused by natural features, leading to inaccurate predictions like mistaking yard debris for other objects. Fixing these specific edge cases was a complex and time-consuming process. They used Labelbox to address this.

Labelbox's solution involved implementing an active-learning cycle within Cape's training pipeline. This process identified the model's low-confidence predictions and prioritized them for review and correction by data scientists. This targeted approach, supported by Labelbox's queue management, allowed for rapid fixes. The result was an estimated 30%+ increase in time savings and the avoidance of months of custom development work.

Cape Analytics