Insights from day-to-day engineering
What AI actually does in engineering design today, how EU data privacy works in practice, and what engineering teams can learn from each other.
Standards search in mechanical engineering: the 2026 guide
Design engineers lose hours searching standards content. Where teams search today, what it really costs, and how AI speeds up standards search.
On-Prem or EU Cloud for AI on Engineering Data
On-premise is treated as the safe option. What it mainly does is move the responsibility. What the decision actually hangs on, and which requirement genuinely forces on-premise.
Searching Standards With AI: Why the Citation Decides Whether the Answer Is Usable
An AI answer about a standard without a file and page reference is not merely inconvenient in engineering. It is unusable. Why that is, and how to recognise a system that holds up.
Roschiwal + Partner: Making a Large Document Archive Accessible, Drawings Included
Data that has grown over years is not a search problem, it is an access problem. How Roschiwal + Partner put its own archive to use, and why CAD and drawings are the harder part.
A chatbot for technical documentation: what it actually has to do
A chatbot on your own technical documentation sounds straightforward. In practice its usefulness is decided at four points, and generic chatbots fail at all of them.
What is design automation?
The term covers very different things, from a parametric template to a full configurator. A look at the stages, and at the precondition that is almost always underestimated.
ChatGPT for internal documents: the 2026 GDPR check
Uploading internal documents to the public ChatGPT? Risky. The 2026 GDPR check: what is allowed, where the pitfalls are, and which alternative cites its sources.
AI for technical documentation that cites its sources
How an AI backs every answer about your technical documents with a source: the exact file and page. Verifiable, auditable, no unnoticed hallucinations.
3 Questions Every New Engineer Asks. And No One Can Answer Quickly.
New engineers ask the same three questions every day. The knowledge exists but cannot be retrieved. How KoAssist solves the onboarding problem.
GISCON: Finding Project Knowledge Across Departments in Special-Purpose Machine Building
GISCON builds special-purpose machines with every discipline in-house. That is exactly why project knowledge ends up spread across many departments. How the team now finds it in one place.
Multimodal AI: Why Images Matter as Much as Text in Engineering
Technical drawings, calculations, screenshots from CAD systems: engineering knowledge is largely visual. Why AI systems that only understand text fall short in mechanical engineering.
The Biggest AI Advantage in Mechanical Engineering Is Already on Your Servers
Mechanical engineering companies in the DACH region sit on a data treasure the rest of the world does not have. Why almost no one uses it, and how AI changes that.
What AI Actually Delivers for Engineering Teams Today
AI assistants promise a lot. What of that really works in everyday engineering, and where the limits have to be drawn honestly.
EU Hosting and AI: What It Really Means for Engineering Data
Engineering data is often highly sensitive. What EU hosting actually guarantees for AI systems, and which questions procurement teams should be asking.