An engraving company wants to automate quality control for engraved plaques. They have an S3 bucket with images of defective plaques that should be rejected. Low-confidence automated predictions must be routed to an internal review team using Amazon Augmented AI (A2I). Which solution meets these requirements?

The correct solution involves Amazon Rekognition because it is a computer vision service specifically designed for image and video analysis, making it suitable for identifying defects in engraved plaques. Amazon A2I is correctly used to route low-confidence predictions for human review, ensuring accuracy. A private workforce is appropriate for internal review teams, maintaining data confidentiality and control. Amazon Textract is incorrect because it is designed for extracting text and data from documents, not for general image analysis or defect detection. Amazon Transcribe is incorrect as it converts speech to text and is irrelevant for image-based quality control. AWS Panorama is incorrect because it is an appliance and SDK for computer vision at the edge, not a general-purpose cloud service for image analysis and routing to A2I. Additionally, using Amazon Mechanical Turk for a private internal review team is incorrect; a private workforce is the appropriate A2I option for internal teams.