Next steps to your hackathon
- Intro call. Clarify starting point, teams and goals.
- Tools and challenges call. Pick the engineering challenge, set the stack.
- Finalization and preparation. Set up access, repos and data, GDPR compliant.
- Tool workshop and sprint. Hands-on on your own case with live coaching.
- Result pitches. Each team shows its prototype.
Industry challenges for IT and SaaS
Typical challenges are a code review assistant, automated test generation, an internal knowledge bot for the docs, a support ticket triage agent and a release notes generator from commits. Plus classics like an email reply assistant and meeting-to-action-items. Your challenge comes from your real backlog.
AI training vs. AI hackathon (short)
A classic training ends with a certificate and retention that is unclear without transfer measurement. The hackathon ends with a running tool in your pipeline and measurable practical transfer. For technical teams the difference is especially clear, because results show up in the code immediately. See the full comparison at AI training vs. AI hackathon.
Do we work on our real code? Yes, if you want. You keep full data sovereignty, we work GDPR compliant in your environment, repos and access stay under your control. Alternatively we start on a representative example and transfer the result afterwards.
What does the team keep after the week? A working prototype in your pipeline, a team that uses the stack confidently, plus an internal AI literacy measures record. More on the process at how we work and formats at formats.
FAQ
Do developers and support teams need the same AI training? No. Developers face different tasks, risks and tool boundaries than support, product or customer success. Learning paths should follow roles and real workflows.
How do you prevent training without workplace transfer? Tie every learning unit to an approved real task and finish with a reviewable artefact, review and accountable next step.
Which governance topics matter for SaaS teams? Data classes, tenant isolation, source code, secrets, model access, human oversight, logging and the approval process for production use.
