“Appen treats AI research as a managed operation: we combine context engineering, calibrated LLM-as-a-judge evaluation, synthetic data generation, and expert-in-the-loop review into a single workflow embedded directly in your research program, run to measurable quality standards rather than staffed by headcount alone.”
“Standard data annotation labels existing data points in isolation. Our research services function as embedded research operations: we run the context engineering, calibrated evaluation, and benchmarking work your team would otherwise have to staff and manage in-house, positioned as an extension of your research program rather than a commodity labeling vendor.”
“We calibrate and validate every LLM judge against human-rater ground truth before it is deployed at scale, measuring inter-annotator agreement with Krippendorff's alpha and Cohen's kappa. Agreement scores and audit trails are documented for every deployment, and human validation is retained wherever the stakes are highest.”
“All sensitive enterprise data is PII-scrubbed before it enters any workflow and processed only within governed environments, with practices aligned to ISO 42001 and applicable data protection regulations such as GDPR and CCPA.”
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