Annotation engineering in the field

Standard bounding-box pipelines break down when objects are 60–90% occluded. This case study covers the multi-pass annotation protocol, inter-annotator agreement thresholds, and quality gates that produced production-grade training data for an autonomous vehicle perception model operating in dense urban environments.

OCR field accuracy drops to 41% on barely legible financial documents. This case study covers the human-in-the-loop correction protocol, CA-qualified annotator selection, and three-tier QA architecture that raised accuracy to 99.1% — reducing false loan rejections from 8.2% to 0.9%.

Standard annotators achieve 54% accuracy on the hardest crop-weed confusion pair — barely above random chance. Expert agronomist annotators reach 89% on the same pair. This case study documents the methodology, calibration protocol, and 1,140:1 ROI on expert annotation investment.

Preference annotation for agentic AI requires evaluating entire reasoning chains — not just individual responses. This case study details the annotation rubric, logical coherence scoring protocol, and inter-annotator calibration approach used to build RLHF preference data for a multi-step autonomous web browsing agent.