Case Study: Cape Analytics achieves an API-first automated training-data workflow and cuts setup from days to 10 minutes with Labelbox

Customizing an optimal training data workflow using an API-first approach

Cape Analytics, a provider of AI-powered geospatial property data, needed enormous amounts of high-quality labeled imagery to train models that assess property features and emerging risks (e.g., roof condition, vegetation, wildfire zones). To replace manual, labor-intensive workflows they partnered with Labelbox, using Labelbox’s API-first training data platform (including its GraphQL API and the Consensus QA tool) to automate and scale dataset import, labeling, and metadata capture.

Labelbox implemented an API-driven workflow that let Cape programmatically import/export labeled data, preserve rich metadata, customize Consensus voting and labeler evaluation, and inject votes or pull intermediate results into their ML pipeline. The result: Labelbox reduced what previously took several days plus an extra day to fetch data down to a roughly 10-minute process, improved label quality control, and freed Cape Analytics’ engineering and data science teams to focus on delivering AI products rather than managing training data infrastructure.

Cape Analytics