You do not recognise good AI training by the feedback form, but by what happens in daily work the Monday after. With us your team learns the tools by solving a real case from its own day, and goes home with a running prototype instead of a sheet of paper. We plan scope, challenges, roles, data, tool access and the run of show so your training can be ready to start within one week. What ships and when depends on the agreed scope.
Audience: business and leadership teams from SME to enterprise that want to apply AI, not just understand it. No coding skills required.
AI training is guided upskilling that enables employees to use AI tools confidently in their own daily work. In our format it runs as a facilitated hackathon in which teams build a prototype on real data. The result is practical skill plus a documented literacy measure, not an attendance record.
The problem AI training has to solve
Most companies no longer have a knowledge problem. Their people have heard of ChatGPT, some attended a seminar, and daily work barely changes. That is rarely about motivation. It comes down to four points a lecture cannot fix: the transfer to the real, often sensitive work case breaks; there is no occasion to practise; real data is locked until IT and privacy are involved; and there is no proof at the end that anyone works differently. Training that works flips the order. Instead of learning first and maybe applying later, teams build on a real case from hour one. The learning happens in the building.
What the AI training includes
Our AI training is a guided work sprint for business teams, not a developer competition. It starts with a compact tool workshop on the stack, then each team builds on its own tightly framed challenge. Participants work with Cursor, Lovable, n8n, Gamma, Figma Make, Claude Code and Custom GPTs on their own tasks. The learning path has four stations: foundations and tool workshop that bring everyone up to speed, challenge scoping with a named user and desired output, a guided build phase, and a pitch with a handoff to a named owner.
Learning goals and benefits
The goal is not a good feeling on Friday afternoon. The goal is that your team visibly works differently: every participant can operate the right stack for their own task, a working artifact you own and keep running, practical AI skill that formed on a real case and therefore sticks, a documented AI literacy measure toward EU AI Act Article 4, and a realistic picture of where AI actually carries in your process and where it does not.
Who it fits, and who it does not
| Good fit when | Not a fit when |
|---|---|
| a team has real recurring friction (manual reports, slow replies, scattered knowledge) | you only want broad AI awareness in a large plenary |
| an owner will continue after the training | no one can take responsibility afterwards |
| data and tool access can be approved in principle | real data cannot be touched for legal reasons under any circumstances |
| you need a visible first output, not just a certificate | a certificate without application is enough |
Deliverables
Pre-scoping with per-team challenge design, a curated tool stack per challenge including access, a facilitated tool workshop plus build sprint with support throughout, at least one working prototype per team, a handoff document per prototype with owner, access, open risks, acceptance criterion and next step, a skills library with the prompts, flows and templates from the sprint, and documentation of the AI literacy measure for participants.
Formats
| Format | Duration | Participants | For |
|---|---|---|---|
| Spark | 1 day | up to 10 | one focused prototype, first proof of learning |
| Ignite | 2 days | 10 to 15 | several challenges in parallel, broader enablement |
| Blaze | 3 to 5 days | 15+ | multiple teams, deeper prototypes, production-ready handoff |
Pricing on request for now. The right shape depends on team size, number of challenges and how deep the handoff needs to be.
AI training vs. AI hackathon
| Criterion | Classic AI training / seminar | AI hackathon |
|---|---|---|
| Output | certificate, attendance record | working prototype you keep |
| Learning mode | lectures, slides, example prompts | hands-on with real data and tools |
| Transfer to daily work | unclear without measurement | visible via prototype, usage and handoff |
| Ownership afterwards | rarely defined | named owner with a next step |
| ROI evidence | hard to show | tangible artifact as a starting point for your own measurement |
| Time to first result | weeks to months | ready to start in 1 week, outcome depends on scope |
The table compares ways of working, not vendors. From a training you take home a sheet of paper, from a hackathon a prototype that already runs. Reliable figures only come from your own baseline. Full comparison on AI training vs. AI hackathon.
How the training can add up (a model, not a client number)
An honest view uses your own figures, not a borrowed case number. Three inputs and one formula: weekly time for a recurring manual task, the share a prototype realistically removes, and internal hourly rate times affected people. Formula: hours saved per week times hourly rate times 45 working weeks times people, minus the one-off cost of the training and rebuild weeks. If a task costs four hours per person per week and a prototype removes half, that is two hours; across ten people and a year that is an order of magnitude you can test the investment against honestly. We run exactly this in the discovery call with your real numbers. Interactive calculator: /en/ki-schulung/tools/roi-calculator/.
Social proof
Teams from companies such as YOYABA, Onventis and NavVis have worked with us. Names, logos, workshop photos and public feedback from these teams are in the case studies.
FAQ
What is the difference between AI training and an AI hackathon? A classic AI training conveys knowledge through slides, an AI hackathon lets your team apply the knowledge and build something with it. After training you keep a certificate, after the hackathon a working prototype and a transfer visible through prototype, usage and handoff.
How many participants does the AI training suit? From small teams to large groups. Spark covers up to 10 people, Ignite 10 to 15, Blaze 15 and up. With larger staff, several teams work in parallel on their own challenges. We settle the right format in the intro call.
Is the AI training suitable for beginners with no prior knowledge? Yes. The tool workshop at the start brings everyone up to speed, whatever level they start at. We work with tools like Lovable, ChatGPT and n8n that have low entry barriers. Nobody needs to code to build real results.
How much does AI training cost? The price depends on format and team size. We do not put flat prices on the page yet, because every engagement is tailored differently. You get a concrete quote on request in the discovery call at https://cal.com/jamboula/ai-hackathon.
Do we get a record for participants? Yes. You get documentation of the practical AI literacy measure that supports your internal record keeping. Unlike a plain certificate, it is backed by a format that actually builds competence, evidenced by the prototypes created.
What happens to our company data? Data access and boundaries are settled before the sprint with your IT and privacy. Only what is approved gets used, and the boundaries are documented. On request everything stays in your infrastructure.
How fast can we start? We plan scope, challenges, roles, data, tool access and the run of show so the training can be ready to start within one week. Delivery time and outcomes depend on the agreed scope.
Legal note: A hackathon and the competence record described here can document practical AI literacy measures, but they are not an officially mandated certificate and do not automatically guarantee compliance with Article 4. The company must assess its overall program for the relevant roles, context and risks.
