The quality assurance and validation process ensures that labeled datasets meet the required standards of accuracy and consistency before being delivered to the client. Quality assurance involves manual reviews by experts, spot checks of annotations, and the application of automated validation scripts to detect and correct errors. The final output is a quality-assured dataset that aligns with client expectations and the specific requirements of the AI/ML model development pipeline.