How to Train AI on ATS Data with Unified's ATS API

Training AI on Applicant Tracking System (ATS) data often encounters challenges due to inconsistencies across different ATS platforms, rather than issues with the AI models themselves. Unified's ATS API aims to address these challenges by providing normalized data objects and consistent retrieval semantics without inventing missing fields or reconstructing stage history. The process involves building an AI-ready dataset using normalized ATS objects such as jobs, candidates, applications, interviews, and documents, while handling provider variability explicitly and keeping the dataset up-to-date. The approach requires creating a stable foundation for feature engineering and labeling by joining data from various ATS objects and using structured signals that are broadly available. Labels and outcomes are constructed based on observable data, without assuming the presence of fields that aren't reliably populated across providers. The strategy emphasizes honesty about missing data and the need for continuous updates through incremental refreshes and retraining loops. By leveraging these practices, the API facilitates the development of robust AI models that account for the inherent limitations and inconsistencies of ATS data.