Your team doesn't need another webinar. They need someone who has actually trained models and shipped AI systems to stand in the room, show them how it's done, and answer the hard questions from experience.
We run corporate AI trainings from one to five days — for engineers, for leadership, for IT and platform staff, or for the whole workforce at once. Hands-on wherever possible: your people leave having built something, not just watched something.
Who This Is For
- Engineering teams — backend, data, and AI engineers — that need to go from "we call an API sometimes" to building RAG systems, agents, and evaluations properly
- Leadership teams that need a grounded picture of what AI can do for the business — and a defense against vendor snake oil
- IT, platform, and MLOps teams who suddenly have models to run in production and no playbook for running them
- Companies rolling out AI tools who want the whole org on a shared vocabulary before the arguments start
AI Team Training 1–5 days · on-site or remote
A private workshop for your team — seminar, short course or multi-day programme, on-site or fully online. Pick a format:
- 1 day — Foundations & strategy. How LLMs actually work, what they can and can't do, where AI pays off in your business. For leadership or mixed audiences.
- 2–3 days — Hands-on building. Prompt engineering, working with model APIs, building RAG systems and agents, evaluating whether any of it actually works. For engineers and technical teams.
- 5 days — Deep dive. The full stack: from prompting through fine-tuning, deployment, and evaluation pipelines. Your team ships a working internal project by Friday.
Curriculum customized to your industry and, where practical, built on your own use cases and data. Priced per format on the intro call.
Not sure which format fits? That's what the free intro call is for.
The Courses
AI Literacy for the Whole Company 1 day · everyone
The session to run before the arguments start. What language models actually do, how they fail, and what the words mean. Your people leave with a shared vocabulary and a realistic picture of what AI can and can't do.
AI for Leadership: Strategy & Judgement 1 day · executives
For the people who sign off on the budget. Where AI pays off, where it quietly burns money, how to read a vendor demo, and what to ask before approving a project. You leave able to judge AI proposals — including ours.
Building with LLMs: APIs, Prompting, Evaluation 2 days · engineers
Hands-on from hour one: model APIs, structured outputs, prompting that survives edge cases, and the evaluation discipline most teams skip. Your engineers leave with working code and an evaluation harness for it.
RAG & Agentic AI in Production 3 days · engineers
Retrieval over your own documents, and agentic systems that do more than demo well: chunking and embeddings, hybrid search and rerankers, tool use, and the failure modes nobody warns you about. Your team leaves with a retrieval system and an agent that survive contact with reality.
AI for IT, Platform & MLOps Teams 2 days · IT, platform, ops
The part that starts after the prototype works: deployment, cost control, monitoring, evaluation in CI, and governance. MLOps for the teams who now have models to run and no playbook for running them.
Fine-Tuning & Self-Hosting 5 days · ML engineers
When fine-tuning beats prompting and how to do it, from LoRA to full training — then serving the result on your own hardware with open-source tooling such as vLLM. Your team leaves with a fine-tuned model running on their own infrastructure.
We also do this as a project →
Courses can be combined — a day for leadership followed by two or three hands-on days for the engineers, for example — and the curriculum is adapted to your industry and your use cases.
We teach principles, not frameworks — the parts that are still true when the tooling changes. The hands-on work runs on whatever your team already uses: commercial APIs or open-weight models, in your cloud or on your own hardware.
Your trainers are the same senior engineers who deliver our client work — people who have trained language models from scratch and shipped AI systems at some of the world's largest tech companies. You meet your trainer on the intro call.
Want the training to end with something real in production? Pair it with an AI POC Sprint. Rolling out AI coding tools to your engineers? That's AI Engineering Workflows.
FAQ
Is the training for technical or non-technical audiences?
Both — but not with the same material. We run executive sessions in plain language (what AI can and can't do, where the money is, how to judge vendors), deeply technical tracks for engineers (APIs, RAG, agents, evaluation, fine-tuning), and mixed formats where leadership and engineers build a shared vocabulary. Tell us who's in the room; we build the day around them.
How long is a training and what fits in each format?
From 1 to 5 days. One day covers fundamentals and strategy for one audience. Two to three days adds hands-on building: your people leave having built a working RAG system or agent. Five days is a deep dive through the full stack, from prompting to fine-tuning and deployment. We help you pick the format on the intro call.
Is it hands-on or lecture-style?
Hands-on wherever the audience allows it. Engineers write code from hour one and leave with working projects, not just slides. Executive sessions are discussion-driven with live demonstrations on examples from your own business — we build something in front of you, with your data where possible.
Can you customize the curriculum to our industry and our data?
Yes, and we strongly recommend it. Generic exercises teach less than examples from your own domain. Before the training we review your use cases and, where practical, build the exercises around your industry, your documents, and your actual problems.
Do you run trainings on-site or remote?
Both. We're remote-first with home bases in Brisbane and Berlin and travel for on-site trainings worldwide. Multi-day hands-on formats usually work best in person; shorter or distributed-team formats work well over video.
Who does the teaching?
The same senior engineers and PhDs who do our consulting work — people who have trained language models from scratch and shipped AI systems to production. You get practitioners answering hard questions from experience, not trainers reciting someone else's deck.
What happens after the training?
You keep all materials, code, and exercises. Most teams follow up with office hours, a second session a few weeks later, or a build engagement where we develop the first real project together — see Custom AI Development.
Before you hire anyone, read how we work. We publish our operating manual in full — how discovery runs week by week, why we build the evaluation before the model, what we test before anything reaches production, and what you own at the end. Read the Boring AI Playbook →
Want your team trained by practitioners?
Book a free 30-minute call. Tell us who's in the room and what they should be able to do afterwards — we'll propose a format.
No pitch deck, no obligation. If AI is the wrong answer for your problem, we'll tell you that too. Not ready for a call? Send us a message instead.