Scaling data annotation using vision-language models to power physical AI systems

Critical labor shortages are constraining growth across manufacturing, logistics, construction, and agriculture. The problem is particularly acute in construction: nearly 500,000 positions remain unfilled in the United States, with 40% of the current workforce approaching retirement within the decade. These workforce limitations result in delayed projects, escalating costs, and deferred development plans. To address these constraints, organizations are developing autonomous systems that can perform tasks that fill capacity gaps, extend operational capabilities, and offer the added benefit of around-the-clock productivity.

Building autonomous systems requires large, annotated datasets to train AI models. Effective training determines whether these systems deliver business value. The bottleneck: the high cost of data preparation. Critically, the act of labeling video data—identifying information about equipment, tasks, and the environment—is required to make sure that the data is useful for model training. This step can impede model deployment, which slows down the delivery of AI-powered ...