Agentic VLM applications in manufacturing

Vision Language Models (VLMs) introduce net-new functionality to vision workloads in manufacturing that traditional computer vision models simply do not offer (e.g., open-vocabulary detection, in-context-learning). Even so, fine-tuned models like YOLO offer a level of precision and recall that today's VLMs struggle to match out-of-the-box. Through agentic harnesses that coordinate calls to VLMs, we can start to deliver similar reliability on manufacturing-relevant tasks (e.g., many-class, many-instance detection), while also supporting the net new functionalities (e.g., multimodal search) that make VLMs distinct. In this talk, we walk through the design of these harnesses, how you serve them efficiently, and how they deliver value in manufacturing. Subraiz Ahmed is a member of the Technical Staff at Perceptron AI. He builds the infrastructure to serve frontier vision models. He previously founded a series of startups. #FiftyOne #Voxel51 #DataCuration #AIAgents #ComputerVision #MCP #YOLO #DataCentricAI