Boca Raton, Fla. – September 15, 2026 -- deviceWISE, a Telit Cinterion company, will run three live production demonstrations at IMTS 2026 in Chicago, September 14-19, showing agentic AI that detects faults on a live robotic cell and guides automated recovery.
Three live demonstrations show layered factory automation at Booth 236475
Visitors will see visual inspection, robotic sorting and automated assembly running on the deviceWISE platform. A visual inspection station using deviceWISE Visual Intelligence will check circuit boards and other objects for defects or missing components. A robot will sort colored cubes in an agentic AI and digital twin demonstration, while a second robot performs a FANUC AI auto assembly of industrial parts.
Fault Detection & Recovery replaces manual diagnosis on robotic lines
Both robotic demonstrations will use Fault Detection & Recovery to reproduce a fault and show automated recovery through the platform. Fault handling on robotic lines is typically manual, requiring an operator to notice a stoppage, diagnose the cause and work out recovery steps. In the demonstrations, that analysis happens at the edge, with the platform generating the recovery procedure and guiding the operator, removing dependence on which operator is on shift.
deviceWISE Intelligence Suite applies AI agents across four operational areas
The deviceWISE Intelligence Suite uses industrial AI agents to analyze production data and support decisions at the edge, covering fault analysis and recovery, work process optimization, operating procedure compliance and workstation monitoring. Fault Detection identifies and diagnoses equipment or process faults and recommends or executes recovery steps. Workstation Sentinel continuously monitors the production line, comparing observed activity against standard operating procedures to flag issues such as incorrect parts or improper feed.
Platform integrates NVIDIA Metropolis blueprint for visual intelligence at scale
deviceWISE Intelligence Suite integrates the NVIDIA Metropolis Video Search and Summarization Blueprint. Using Model Context Protocol and NVIDIA NIM microservices, the platform pairs camera data with live machine data and process state, interpreting visual events against actual line activity rather than in isolation, then routing responses to the appropriate system or operator.