AI for production and quality
AI literacy training under the EU AI Act, knowledge management, computer vision, and predictive maintenance – hands-on, from Tyrol, for industry.
AI training for industrial companies – required by Art. 4 EU AI Act
Since 2 February 2025, Article 4 of the EU AI Act requires companies that use AI to ensure their staff have sufficient AI literacy. I deliver this hands-on – training from the shop floor, not legal advice.
- What AI can and cannot do in your operations – from the shop floor, not the lecture hall
- Everyday risks: GDPR, shadow AI, hallucinations
- Concrete rules for your teams' daily work
- Documented training certificate for your compliance records
For production managers, plant managers, QA, maintenance, and administration – mixed groups of up to 12 people.
Tyrol & western Austria: on-site – 30–60 minutes from your shop floor. Rest of Austria & DACH: remote or by arrangement.
flat rate, incl. materials and certificates of attendance, plus travel outside Tyrol
Practical AI Solutions
Start with the training or the workshop – both at a fixed price. Implementation from knowledge management to predictive maintenance, if it makes sense afterwards.
Getting started
Fixed price, short buying cycle – this is where most companies start.AI Training
Required by Art. 4 EU AI Act
Half-day in-house training for industrial companies – hands-on from the shop floor, with a documented certificate for your compliance records.
- Approx. 4 hours, on-site or remote
- Mixed groups up to 12 people
- Training certificate included
AI Potential Workshop
1 day on site, incl. preparation call
One day at your plant: prioritize use cases, check your data, define a roadmap – with a results document (3–5 pages) as a basis for decisions.
- Prioritized use case list
- Data readiness check
- Rough implementation roadmap
Implementation
Project work after the workshop or pilot.Knowledge Management
Private RAG systems that make your documents searchable via AI chat – with TyrolAI Docs as the foundation.
- Chat with documents
- On-Premise / GDPR
- Microsoft SSO
Quality Control
Computer vision for automatic defect detection and quality inspection in manufacturing.
- Visual inspection
- Anomaly detection
- Process support
Maintenance & Optimization
Predictive maintenance and process optimization – predict machine failures, optimize parameters.
- Vibration analysis
- Anomaly detection
- Process optimization
The Reality Check
From first call to AI in production – step by step, no detours.
To be clear: the initial consultation is free – from the workshop onwards, everything has a price.
Initial consultation
We clarify the trigger, the goal, and your data – and whether it is an AI topic at all.
Potential workshop
One day on site: prioritize use cases, check data readiness, define the roadmap – incl. preparation call.
Pilot
I build a pilot that tests your use case in real operations – with measurable KPIs.
Implementation & operations
If the pilot convinces: production deployment in your infrastructure – with monitoring, maintenance, and documentation.
Insights that are actionable
Short, practical articles on AI in production, quality, and service.
TyrolAI Docs: Why I built my own RAG system for industry
Enterprise RAG sounds like a solved problem. In practice, most off-the-shelf solutions fall apart at GDPR, Active Directory, or the question of who is allowed to see which document. That is why I built TyrolAI Docs.
AI in metalworking: What actually works and where to start
Metalworking offers ideal conditions for AI: repetitive processes, measurable quality, and existing sensor data. But the entry point has to be realistic.
Predictive Maintenance in Manufacturing: What AI Can Actually Do
AI-based predictive maintenance sounds promising - but not every machine needs it. An honest look at the technology, data requirements, and how to start.
What does AI really cost in manufacturing? Honest answers instead of sales pitches
Nobody likes to talk about AI costs in manufacturing. I do. An honest overview of what drives the machine learning budget and when ROI is realistic.
Computer vision in quality control: What actually works in practice
Computer vision quality control sounds promising but has pitfalls. Four use cases, honest limitations, and what you actually need to run automated quality control in manufacturing.
Edge Computing vs Cloud: Where Should Manufacturing AI Run?
Edge or cloud? The answer is rarely black and white. A practical comparison for manufacturing companies looking to deploy AI on the shop floor.
OPC-UA and AI: How Machines Talk to Algorithms
AI models are impressive in the lab but worthless on the shop floor without machine connectivity. OPC-UA closes this gap - and I explain what the path from PLC to AI model actually looks like in practice.
From idea to AI pilot in 2–6 weeks: the process
Big AI projects often fail because of unrealistic expectations. An AI pilot project in two to six weeks delivers real results with minimal risk. Here is the concrete process.
RAG Systems in Manufacturing: Why Your Maintenance Manuals Need AI
Technical knowledge is trapped in PDFs, Excel sheets, and the heads of experienced employees. A RAG system makes this knowledge accessible through simple questions - locally, GDPR-compliant, and without cloud dependency.
From practice for practice
I'm Simon Kirchebner – an independent AI consultant with a background as a process technician in manufacturing. I know the reality of production: shift work, quality pressure, and robust processes. Based in Tyrol, I serve manufacturing companies across Austria and the DACH region.
As a solo consultant, I advise, train, and implement directly – no project-manager cascades. My approach: clear scope, measurable KPIs, and practical rollout.
Tyrol & western Austria: on-site – 30–60 minutes from your shop floor. Rest of Austria & DACH: remote or by arrangement.
"AI must work on the shop floor."
TyrolAI Docs
Enterprise RAG platform — on-premise, GDPR-compliant, Microsoft SSO, document-level security. Proof that I don't just consult, I build. Available in project work on request.
Where do we start?
Briefly describe what brings you here — I'll get back with an honest assessment.