An AI course that trains on a real project
An AI course built around your real challenge instead of a fictional example, trained directly on your own process with Cursor, Lovable and n8n.
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An AI course built on a practice dataset ends with a few screenshots and a fading memory. This course trains on a real project from your own operation, with a syllabus that builds towards a concrete use case. Your team learns prompt engineering, the right tools and automation by using them to build a solution that keeps running on Monday. We plan scope, challenges, roles, data, tool access and schedule together so your course can be ready to start within a week.
An AI course teaches how to use AI tools. At Corporathon, the syllabus does not run on a practice dataset, but on a real use case from your own operation, so the result is a working prototype and a documented competence record. The skill stays because it emerged from real work.
The problem this course solves
Standard AI courses work on a fictional example, because that works for many participants at once. That is exactly the weak point. A fictional example creates no link to your own daily work. Four things get lost this way.
- The transfer is missing. An example prompt for a made-up online shop does not fit your real process. Nobody takes care of the translation.
- No result. A course ends with knowledge, not a tool. On Monday, the process is unchanged.
- No context. On a practice dataset, you learn clicks, not the judgement of where AI really carries weight in your own process and where it does not.
- No evidence. A course completion proves attendance, not application.
Corporathon turns the real use case into the syllabus. The course and the project are the same thing, so both stick.
What the course covers, specifically
This is not a developer course. It is a guided learning path for business teams who cannot code and do not need to. Participants work with Cursor, Lovable, n8n, Gamma, Figma Make, Claude Code and custom GPTs on their own data. The syllabus is built around the challenge, not an abstract curriculum.
A run has four building blocks that build on each other:
- 1A project instead of a practice dataset. The learning goal becomes a real, tightly scoped use case with a named user and a desired output, for example "turn 40 weekly proposal PDFs automatically into a comparison table".
- 2Tool workshop. Each group learns exactly the tools and prompt patterns its project needs.
- 3Build phase. The teams build with guidance. We sit alongside, unblock issues and show the next step.
- 4Pitch, evidence and handoff. Each team shows its result, hands it to an owner, and the course completion carries real hands-on evidence.
Benefits and goals
The goal is not a full notepad. The goal is that what is learned emerged on a real case and is therefore applicable.
- Prompt engineering and tool confidence, learned on your own project.
- A working artefact that belongs to you and keeps running, instead of screenshots.
- A clear picture of where AI carries weight in your own process and where it does not.
- A documented AI competence measure as a building block for Article 4 of the EU AI Act.
Who it fits, and who it does not
| Good fit when | Not the best fit when |
|---|---|
| a team wants to learn on its own project, not a practice dataset | a plain basics course for individuals is enough |
| there is a real, recurring use case | no concrete task exists yet |
| data and tool access can generally be approved | real data must never be touched for legal reasons under any circumstances |
| an applicable result should stand at the end | a course completion without application is enough |
Services and deliverables
- Upfront scoping with project and challenge scope per group.
- A curated tool stack and matching prompt patterns per challenge, including access.
- A facilitated build sprint with guidance throughout.
- At least one working prototype per team.
- A handoff document per prototype with owner, access, open risks and next step.
- A course completion record with hands-on evidence and a documented competence measure per participant.
Anyone who needs more than one day will find three days from the basics to Claude Code, with a capstone task and a Corporathon certificate, in the Claude Upskilling programme.
Formats as offer cards
Spark
- What you get: 1 day, a project instead of a practice dataset, tool workshop, handoff document, course record with hands-on relevance.
- You work with: one Corporathon facilitator, the right stack for one challenge.
- Ideal for: up to 10 participants, a team taking its first course on a real case.
- Your investment: on request.
Ignite (recommended)
- What you get: 2 days, several projects in parallel, several prototypes, course records, broader application across the team.
- You work with: several facilitators, a curated stack per challenge.
- Ideal for: 10 to 15 participants, a department that wants to learn AI across several processes.
- Your investment: on request.
Blaze
- What you get: 3 to 5 days, several teams, deeper prototypes, course records with a clear implementation path.
- You work with: a facilitator team, the full tool stack, an optional link to an implementation sprint.
- Ideal for: 15+ participants, several departments with a company-wide learning goal.
- Your investment: on request.
Teams from Adobe, YOYABA, Onventis and NavVis have worked with us. Prices on request for now; the scope depends on team size, number of projects and depth of handoff.
AI course on a project vs. standard AI course
| Criterion | Standard AI course | Corporathon AI course on a project |
|---|---|---|
| Learning basis | a practice dataset | a real use case from your operation |
| Result | knowledge, screenshots | a working prototype you keep |
| Transfer | open | built into the course itself |
| Evidence | course completion | a course record with hands-on evidence |
| Proof of ROI | hardly provable | a tangible artefact as the starting point for your own measurement |
| Time to first result | weeks | ready to start in 1 week, result depending on scope |
The table compares course types, not providers, and deliberately contains no invented percentages.
How the course can pay off (a model, not a customer figure)
A course on a real project brings two effects: a learned skill and a finished tool. The model has three inputs: time per week for a recurring task, the share the prototype built in the course takes off, and internal hourly rate times people affected. Formula: *hours saved per week × hourly rate × 45 weeks × people − one-off cost*. A worked example as a model. If a task costs three hours per person per week and the prototype takes off a third of that, that is one hour per person. At a rate of 60 euros and eight people, that is 1 × 60 × 45 × 8, so 21,600 euros a year in freed-up capacity, on top of the skill learned. We do the real calculation in the discovery call.
Interactive curriculum sketch: /en/ki-kurs/tools/curriculum/.
Social proof
Teams from Adobe, YOYABA, Onventis and NavVis have worked with us. We show their names and logos as references and, where approved, workshop photos and public feedback. We deliberately publish concrete figures only once the source, method and period are documented and approved. Everything at /en/case-studies.
The process as an animated timeline, in 1 week
| # | Step | Owner | Output |
|---|---|---|---|
| 1 | Intro call | Corporathon + sponsor | clarified learning goal, rough scope, date |
| 2 | Tools and challenges call | Corporathon + team leads | scoped projects, tool choice per challenge |
| 3 | Finalising information and data | Client (IT, data protection) | approved data access, documented limits |
| 4 | Hackathon preparation | Corporathon | prepared environments, access, schedule |
| 5 | Tool workshop | Corporathon + teams | teams can operate their stack |
| 6 | Hackathon sprint | Teams (guided) | working prototypes |
| 7 | Result pitches and handoff | Teams + owner | results, handoff documents, course records |
Your Corporathon, in just 1 week.
30-minute discovery call: clarify the need, pick a challenge, set the schedule.
The calendar is embedded via Cal.com and only loads external content once you ask for it.
Frequently asked questions
Short answers to the questions that come up most before booking.
A normal course trains on a practice dataset and ends with knowledge. This course trains on a real project from your operation and ends with a working prototype plus hands-on evidence. That is why the skill stays applicable.
No. The course is built for mixed teams. Some of the tools work without code, the rest gets close guidance. We choose the stack so the challenge is solvable without programming knowledge.
From small teams to over 15 people. Spark fits up to 10 participants, Ignite 10 to 15, Blaze 15 and more. We set the scope in the discovery call.
Yes. Every participant receives a course record with hands-on evidence attached, because the course happens on a real project and a prototype emerges.
We clarify data access and limits with your IT and data protection team before the start. Only what is approved gets used, and the limits are documented.
The course can document a practical AI competence measure and so contribute to Article 4. It is not an official certificate and does not guarantee automatic compliance.