Uli Palli

TL;DR: Standardization in visual inspection is where AI shines.  AI models, once trained on diverse, well-labeled examples of good parts and defects, apply the same quality criteria every time: across shifts, lines, and even plants. Unlike human inspection, which varies with fatigue, experience, and interpretation, AI delivers consistent, repeatable calls and a clear audit trail. […]

TL;DR:Humans and AI on the shop floor work best as partners, not rivals. AI excels at speed, consistency, and pattern recognition in areas like visual inspection and predictive maintenance, while humans bring context, judgment, creativity, and problem-solving. Instead of “AI versus people,” the most effective setups use humans and AI together: AI handles repetitive, high-volume

TL;DR: Generative AI in manufacturing is racing up the hype cycle, and manufacturers are feeling pressure to “do something with GenAI” long before the technology is ready for safety- and quality-critical shop-floor use cases. While generative models are powerful for text, content, and knowledge tasks, they still hallucinate and lack the determinism needed for visual

TL;DR: Human visual inspection performance drops quickly during repetitive work, which makes consistent defect detection across long shifts hard to sustain. Traditional rule-based vision systems tends to be faster but finicky: small changes in lighting or camera position can cause failures, and product changes often mean costly reprogramming. AI-based visual inspection combines human-like pattern recognition

TL;DR:Generative AI for knowledge retrieval in manufacturing works best when it is grounded in your own documentation and tested like any other production system. This post shares eight practical lessons from building a document-backed assistant: documentation quality matters most; ingestion has real-world constraints; models vary in reliability; prompts and guardrails help keep answers on track;

TL;DR:Synthetic data in manufacturing refers to artificially generated data, such as images or sensor signals, that is designed to resemble real production data and can be used to train and test artificial intelligence (AI) models when real labeled datasets are limited. The blog focuses on quality inspection, explaining how synthetic images can increase training volume,