Resources
Technical guides for industrial AI
Decision-oriented analysis and guides for production, quality and data leaders assessing industrial AI projects. No entry-level tutorials: concrete technical trade-offs.
4 articles
Analysis by domain
Quality control in plastic injection molding: structuring an industrial vision project
Acquisition architecture, model selection, false positive / false negative calibration, PLC integration. For quality and production managers assessing feasibility.
Predictive maintenance: equipment selection criteria and data audit
How to prioritize which equipment to instrument, assess the quality of existing history, label past failures and choose between anomaly detection and RUL estimation.
Industrial document intelligence: cloud vs. on-premise OCR, LLMs and exception handling
Criteria for choosing between cloud and on-premise OCR based on sensitive data, validating accuracy on your corpus, sizing human review and integrating into an existing workflow.
Industrial AI: the pitfalls of the POC that never reaches production
Why AI projects stall between prototype and production, the criteria for a viable MVP, and the minimal MLOps infrastructure to start without technical debt.
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