Teaching
Courses that end with something you can measure.
There are two tracks. The engineering track teaches people to build AI systems and test them. The business track is for deciding what to automate, running it, and bringing a team along. Every syllabus below is the one a client receives, including the paragraph that says where the course stops. A course that claims no limits is a course nobody should buy.
- 1–3 days · 7–21 contact hours
- Delivered in English or German.
- On-site in Europe, or remote. Wels / Linz, Austria is the home base.
Engineering track
Build systems you can measure.
Three-day courses for engineers and researchers, each built around one codebase that gets harder to fool as the days go on.
Open the syllabus
01
LLM Engineering
From prompting and statistical inference to reliable RAG systems
How to build an assistant that answers from your own documents, cites where each claim came from, and ships with a test suite that catches it inventing things.
- 3 days · 21 h
- Engineers, data scientists, technical leads and advanced AI practitioners.
- An evidence-grounded assistant with hybrid retrieval, citations, tests and safety controls.
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02
Reinforcement Learning
From Markov decision processes and control to PPO, continuous control and RLVR
How software learns to make decisions by trying things and being scored on the result — the method behind game-playing systems, robot control, and the way modern reasoning models are trained.
- 3 days · 21 h
- ML engineers, researchers, simulation engineers, robotics developers and advanced students.
- A small Gymnasium environment with baseline control, trained policy, ablation and multi-seed evaluation.
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03
Agentic Engineering
From tool calling and MCP to governed, testable agent systems
How to take an AI agent that works in a demo and give it permissions, tests, an audit trail and a stop button — so you can let it near a real system.
- 3 days · 21 h
- AI and software engineers, architects, security specialists and technical product owners.
- A governed research or operations agent with typed contracts, an MCP server, evaluation and red-team evidence.
Business track
Decide what to automate, run it, and bring the team along.
For the people who choose where AI goes in an organisation, the people who automate the work, and the teams whose work changes. One to three days, built on the participants' own processes.
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04
AI Automation
From process maps to agent-built automations a business can run
How to find the work in a business that is worth automating, have AI agents build the automation, and show afterwards that it saves more than it costs.
- 3 days · 21 h
- Operations leads, automation specialists and consultants, analysts, technical product managers and engineers moving into automation.
- One automated business process with a measured baseline, run cost, error handling and a handover pack.
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05
AI Strategy and Economics
What changes when knowledge work gets cheap, and how to plan for it
How to work out which parts of your organisation AI will change first, where the value goes when that work gets cheap, and what to do about it in the next 90 days.
- 2 days · 14 h
- Founders, executives, managers, and product, strategy and operations leads.
- A 90-day AI roadmap with a business case for each initiative, presented for critique.
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06
Team AI Enablement
One day to bring a whole team to a shared, safe level of AI use
A one-day workshop in which a team learns to hand real tasks to AI assistants safely, builds a shared library of reusable instructions, and leaves with a plan to keep practising.
- 1 day · 7 h
- Whole teams in any function, plus the person who will look after the team's AI setup.
- Three automated tasks per participant and a versioned team skills library with a usage policy.
Advisory
The work either side of a course.
Training is usually not the whole problem. Three engagements come up often enough to name.
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Architecture review
A read of an existing LLM or agent system against the same criteria the courses teach: trust boundaries, evaluation coverage, failure handling and what the system does when it is wrong. Ends with a written decision record, not a slide deck.
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Evaluation build-out
Standing up the measurement layer a team keeps postponing: golden sets, regression runs, groundedness and trajectory checks wired into CI, so a model or prompt change stops being a leap of faith.
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Governance evidence
Turning a working system into something an auditor or a board can read: intended purpose, risk register, tool and data inventory, human oversight and a deployment recommendation mapped to NIST AI RMF, ISO/IEC 42001 and the EU AI Act.
Rates and the fuller list of engagements (diagnostics, project delivery, fractional leadership) are on /consulting. They live in one place so the numbers can't disagree with each other.
Book a training
Tell me which course and roughly when. You get a reply from me, not a sales sequence — dates, a quote, and an honest answer if the course is a poor fit for what your team actually needs.