All our training is closed and in-house, for your people only. Beyond that there are two varieties. One is the programme we have taught before. The other we design around your organisation.
In the simpler way, you know what you need, we’ve taught it before, and we come and deliver it. The session is hands-on, runs on your premises, and the exercises come from your sector.
The more interesting way starts from a theme. Perhaps your finance team should be using AI properly, or your engineers keep shipping prototypes that fail in production. Before designing anything, we interview the people who will attend, because what actually hurts is rarely what the training brief says. If your policies allow us near your data, we build the exercises on it. People leave having done the thing on their own material.
We prefer this second route even when you’re buying the standard programme. An hour beforehand with the people commissioning it, covering what the team does today and which decisions the training should change, lets us swap the stock exercises for ones that are actually yours. If your staff use AI at work, your organisation already has a duty under the EU AI Act to take measures supporting their AI literacy. The Act asks that those measures take into account people’s technical knowledge, experience and the context the systems are used in.
Sessions are closed so that people can be honest about what they don’t know, and groups are kept small for the same reason. Hands-on workshops are capped at twelve; Decisions in the Age of AI takes up to sixteen, since the day runs on discussion. The learning outcomes are tied to the participants’ actual jobs. Sessions run in whatever AI tools your organisation already uses, and what people learn carries over when those tools change. A month after the session we follow up, because the measure of a workshop is what people are still doing thirty days later. Three of the forty learners in the case where we tested that measurement looked like the strongest improvers in the room on the day and had kept 21.9% of their gain eight weeks later.
These are the themes we keep coming back to. Treat them as starting points; every session is adapted to its audience.1
For boards and function heads · half a day
The session opens with current systems running on the screen, showing what they can and cannot do. Most of the rest covers reading a vendor’s claim, what a proof of concept is evidence of, and where responsibility sits when a model gets something wrong. The group then works on the decisions it’s actually facing, such as a budget line, a build-or-buy decision, or a supplier already in the building. People leave with a shorter list of things AI might do for them, and better reasons for the items still on it.
For anyone who uses, buys or signs off AI at work · half a day
The session is built on the three commitments that shape all our work, and covers each in practical terms. Models learn from records of past decisions, and those records carry the biases of the people and processes that produced them. A system trained on a decade of hiring decisions learns who used to get hired, not who should have been. In the exercises the group tests a system across groups, finds where the outputs differ, and traces the differences back to the training data.
A system that makes consequential decisions about people also has to be explainable and auditable, and the session covers what 'explainable' means in practice and what an audit trail needs to look like. The third commitment is about scale. Training and running large models consumes significant energy and water, so the group works through why the smallest model that does the job is usually the right one, and what the oversized alternative costs. At the end the team writes a short checklist together, of what to verify before relying on an AI output or signing off a tool.
For finance, operations, sales and HR · one day
Everyone brings a weekly task and rebuilds it with AI: a recurring report, a queue of replies, a pile of documents that somebody has to read. Drafting, summarising, extraction and checking each get a turn, along with the plainer business of writing an instruction that returns the same answer twice. A good part of the day covers verification: which outputs need checking, and how to build that check into the workflow. By the end the task is rebuilt and running.
For power users in finance, procurement, HR or operations · two half-days
After meeting the team, everyone builds a small tool for a task they do the long way round, with an AI agent writing the code. They finish it between the two half-days, with a channel for questions, and bring it back to debug, extend, and work out what to do when it breaks. The disciplines are those of building software without learning a programming language: scoping, testing, documenting, checking what the agent wrote, involving IT, and keeping the tool away from production systems. Plenty of people already use these tools without any of that. The exercises come from Eva’s eight years in financial planning at Procter & Gamble and Coty, much of it in low-code tools.
For controllers and FP&A teams · one day
We work through forecasting, variance analysis, the close and management reporting, all on data shaped like a real ledger. For each one we look at where a model earns its place, where a deterministic rule is better and cheaper, and how to keep an audit trail through either, because a number that reaches the board has to be explainable. The worked examples come from Eva’s eight years in financial planning at Procter & Gamble and Coty. If your team can bring an anonymised extract of their own ledger, the exercises run on that, and the aim is one step of a real close automated by the end.
For engineering teams · two days
Most of the time goes to evaluation: building a test set, measuring against it, and noticing when a change has made the system worse. We teach retrieval and agent design by building tests around them, so the testing framework is in place before anyone starts adding features. Teams with a system of their own bring it and build the harness around that; the others get a prepared system with faults planted in it. Two days is enough to leave with a running evaluation suite and a numbered list of what is failing inside it.
For senior data scientists and quants · one day
We teach the mathematics directly, starting with what a model’s confidence number actually measures and whether it survives a test. Bayesian treatment of model uncertainty and proper scoring rules follow, then the decision layer: when to act on an output, when to route it to a person, and what each threshold costs. Everyone calibrates a model of their own and measures the result. The material comes out of the working paper the two founders are writing on calibrating uncertainty in language-model systems. Uncertainty quantification is Luigi’s research field; the paper is joint work, and so is the session.
For anyone who has to read a vendor proposal · one day
A proposal, a benchmark table and a price list go up on the screen and get taken apart. We work through what the demo actually shows, what the benchmarks were measured on, what the thing costs to run a year in once volumes are real, and how to turn a claim into a question you can test before you sign. Contract terms get a look as well, particularly data handling, exit and who owns what gets built. Anyone with a live proposal can bring it, and if they do, the last stretch goes on drafting the questions to send back.
Each price is for one group, in-house, and includes the time spent with your people beforehand to understand what they need and how they work. You shape the session as much as you want to, from a brief to co-writing the exercises.
Decisions in the Age of AI, half day: €2,500.
The Wider Cost of AI, half day: €2,500.
AI-Augmented Workflows, one day: €4,200.
Agentic Coding for Non-Coders, two half-days: €4,800.
AI for Finance and Controlling, one day: €5,500.
Building with LLMs, two days: from €8,000.
Uncertainty-Aware AI, one day: on request.
Buying AI Without Being Sold To, one day: €4,200.
Prices exclude VAT and are fixed in writing before the session.
The EU AI Act is the first general law on artificial intelligence, and it reaches further than most companies assume, because it regulates not only those who build AI systems but those who use them. Article 4, applicable since February 2025, requires every organisation whose staff use AI at work to take measures to support the development of AI literacy among their staff, taking into account their technical knowledge, experience, and the context the systems are used in. The amended text, following the Digital Omnibus of July 2026, expressly clarifies that no specific level of literacy must be guaranteed. National market surveillance authorities gained supervisory powers from August 2026. Article 50 transparency obligations also apply from August 2026. The rules for high-risk uses (Annex III) arrive on 2 December 2027, with Annex I following on 2 August 2028.
Two consequences matter for most of our clients. First, if your teams use AI at all, even just a model drafting text or an assistant inside a workflow, the literacy duty already applies to you. The European Commission’s own guidance confirms that ordinary commercial use makes a company a “deployer”. No certificate and no curriculum are prescribed, but the duty asks for measures that take into account each person’s technical knowledge, experience and the context the systems are used in. The Commission’s guidance says Article 4 is meant to provide training and guidance fitted to each group’s knowledge, experience and context. The same guidance notes that enforcement risk is higher where an incident can be traced to a lack of appropriate training. Second, some uses carry far heavier obligations. Systems that influence employment decisions, such as screening candidates or evaluating staff, are classed as high-risk.
Our programmes are designed for the applied work your teams do. We provide session records of what was covered, who attended and when. The Commission’s guidance notes that organisations can keep an internal record of the measures they have taken. Whether a particular programme meets a particular organisation’s obligations is a question for that organisation and its counsel. Where a question crosses from practice into legal interpretation, we say so.
Both cases below are invented. They show what tailoring means in practice.
A CFO asks for a workshop on AI-assisted forecasting. In the interviews beforehand, the controllers say the forecast itself is fine; the real pain is the week of manual reconciliation that precedes it. The workshop that eventually runs teaches the team to automate their own close, in their own tools, on their own ledger.
An engineering team keeps shipping LLM prototypes that impress in the demo and fail in production. They bring the current one to the two-day evaluation workshop and spend it building an evaluation harness around their own system. They leave with a measured list of what is actually failing, and a way of testing the next claim before believing it.
A public course teaches a fixed syllabus to whoever signs up, and nobody in the room can talk about their own figures. These sessions are closed and designed after we’ve spoken to the people attending, with the exercises built on your material where your policies allow. In the workshop formats people rebuild a task of their own.
They show the method and the marking rather than results for a client. Ten of the eleven are built on data we engineered ourselves with the answer key held out, and one on a public contract dataset with a published expert key, so every figure could be scored against something instead of asserted. One of them tests the instrument we measure training with, on a simulated cohort whose learning was planted in advance. The eleven cases carry their negative results as prominently as the positive ones.
Two to three weeks for a standard programme, which still includes the scoping conversation beforehand, and four to six weeks if the sessions are built around your own material. Groups are capped at twelve for the hands-on workshops and sixteen for the Decisions in the Age of AI session. If a particular date matters, say so in the first email and we’ll tell you whether it’s possible.
Article 4 asks organisations to take measures supporting the AI literacy of their staff, taking into account their technical knowledge, experience and the systems in use. Our programmes are designed around those factors. We provide a session record of who attended, what was covered and when, and where a client wants more than an attendance record we attach the pre/post measurement, which measures what people can do afterwards. There is no certificate to award, because the Act prescribes none; section v sets out what the duty requires and where the heavier high-risk rules begin. Your own counsel can advise on what your obligations require.
If you know what you need, say so and we’ll tell you whether the standard version fits. If all you have is a theme, send that.