WORLDTECHAI
Property Finder found 96.8% of its vision-AI labels came from its incumbent model
Property Finder found that 96.8% of their vision-AI evaluation labels were inherited from the incumbent model, hiding its true accuracy. See how they built unbiased ground truth, applied evolutionary prompt optimization, and selected a more accurate, cost-effective model on Amazon Bedrock, cutting cost by 86% across 11 image-quality attributes. The engineering team at Property Finder, a digital real estate application, discovered that 96.8 percent of their evaluation labels had been inherited directly from a single model’s predictions rather than verified by human experts. The labels also had errors, but the bias was what blocked objective model comparison.
