Artificial intelligence, machine learning, generative AI and practical AI engineering for industrial environments.
OPC UA is a natural boundary between industrial data sources and AI applications because it can expose structured, contextualised information without embedding AI logic inside…
Read →A digital twin can mean anything from a live dashboard to a high-fidelity simulation. Combining it with AI is powerful only when the twin represents…
Read →Predictive maintenance is often described as a model problem. In practice, the hardest questions are about failure modes, maintenance history, operating context and what action…
Read →Industrial AI projects fail surprisingly often before modelling begins because the data pipeline does not represent the physical process correctly. This guide focuses on the…
Read →LLMs are already useful in engineering, but their value comes from good workflows rather than magic. The important skills are asking precise questions, validating outputs…
Read →Machine vision projects become difficult when they leave the demo table and meet real production variability. Lighting, camera placement, cycle time, traceability and defect definitions…
Read →Anomaly detection is one of the most practical machine-learning problems in manufacturing, provided the engineering team first defines what “normal” means for the process. The…
Read →AI agents can coordinate tools and workflows, but an OT environment punishes casual assumptions about autonomous action. A practical agent architecture keeps observation, reasoning and…
Read →Retrieval-Augmented Generation can make industrial documentation searchable and usable without pretending a language model knows the plant. The engineering challenge is not the chatbot interface;…
Read →Artificial intelligence is entering factories, but it does not replace the control engineer in the way the headlines suggest. The practical changes are more interesting:…
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