Together, the teams developed annotation standards, quality assurance workflows, and a collaborative review process that continuously refined labeling quality while improving efficiency. Workflow experiments helped identify the best balance between speed and precision, while recommendations around data collection and annotation strategy strengthened the overall training dataset.

With a proven data pipeline in place, Glydways can confidently expand its perception capabilities into additional object classes and tasks, and increasingly complex operating conditions. Summing up the collaboration, Moussavi explained, "The most valuable result was the establishment of a reliable and quality labeling pipeline and partnership we can count on for future tasks."