AI Data Labeling & Annotation for SaaS Application Workflows
Worked on data labeling and validation tasks derived from real SaaS application workflows, focusing on structured datasets such as user records, authentication flows, and transactional data. The project involved defining labeling rules and tagging data based on system behavior, including classification of user roles, validation of authentication states, and categorization of transaction statuses. Performed consistency checks on API responses and database records (JSON format) to ensure data accuracy and usability for system logic and potential automation workflows. Identified edge cases such as incomplete records, conflicting states, and invalid data entries, and applied standardized tagging for downstream processing. Maintained data quality by following clear annotation guidelines, ensuring repeatability and consistency across labeling tasks. The dataset scope included multiple structured data samples from application modules such as user management, role-based access systems, and transaction handling.