How to make manuals, datasheets, maintenance reports and standards machine-readable with AI – from PDF and OCR quality through RAG architecture and source attribution to on-premise operation. Technical depth instead of product promises, with an honest account of the limits.
The honest route from a document base to an answer you can verify: which document classes can actually be opened up, where layout parsing breaks down, and why source citation is an acceptance criterion.
In October 2025 Germany's data protection authorities set out how they will judge a RAG system. This article turns the 18 pages into an ingest pipeline, permission filters, a deletion path and a 14-point checklist.
A language model in your own server room is a plant asset, not a subscription: the calculation from user count to GPU, why small models are what make on-premise operation economical, and which cases belong in an EU cloud.
OPC UA, MQTT, S7, MES, ERP and historians: how to get data out of legacy and new machines and into systems you can actually analyse. With decision matrices, fault diagnosis and reference architectures for brownfield plants.
OEE, availability, shop-floor and machine data capture, energy and quality figures: which KPI needs which data source, and how to turn that into a dashboard the shop floor actually trusts. With calculators, formulas and standards references instead of benchmark marketing.
The smallest sensible first step instead of a digitalisation vision: replacing Excel, digital shift handover, paperless production, goods receipt and document processing. Every article names the data source, the target system and the exceptions that make projects fail.
Before the project comes the decision: off-the-shelf or custom, requirement specifications, realistic cost structures for industrial projects and typical use cases in production and maintenance. Decision material that also says when off-the-shelf software wins.