Intro
In healthcare and pharma, documentation, research and regulation eat enormous amounts of time, and handling data is sensitive at the same time. An AI training that stops at slides helps little there, it has to consider data protection from the start. With us your team spends a week building on a real challenge, for example a research assistant for scientific literature or a tool that makes internal documents and policies searchable. Instead of a certificate you keep a working prototype that takes administrative load off your hands. Work happens with tools like Claude, NotebookLM, Custom GPTs and n8n, in a way that lets you keep full data sovereignty and runs everything GDPR compliant. With sensitive data we deliberately start on anonymized examples. Transfer from classic training is unclear without measurement. In the hackathon it is checked through prototype quality, application and handoff. In just 1 week from first contact to result, including an internal AI literacy measures record, which is a real trust anchor especially in heavily regulated industries.
Next steps to your hackathon
- Intro call. Clarify starting point, teams and data protection scope.
- Tools and challenges call. Pick the challenge, set toolstack and data protection.
- Finalization and preparation. Set up access and anonymized 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 healthcare and pharma
Typical challenges are a research assistant for scientific literature and guidelines, an internal knowledge bot for SOPs and policies, a document assistant for preparing reports, a meeting-to-action-items tool and a report autopilot for recurring analyses. Your challenge comes from your real daily work, always within the agreed data protection scope.
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 tool that takes over administrative work, and measurable practical transfer. In an industry with a high documentation load, the time gain is directly noticeable. See the full comparison at /en/comparison/ai-training-vs-ai-hackathon.
FAQ
How do you handle sensitive patient and study data? Very carefully. You keep full data sovereignty, we work GDPR compliant only within the agreed scope. With sensitive data we deliberately start on anonymized or synthetic examples and transfer the result to your environment in a controlled way afterwards.
Is the format suitable for regulated areas too? Yes. We build prototypes that ease your load, not systems that replace regulatory approvals. The internal AI literacy measures record is a strong argument here, because it proves the required AI literacy. More at /en/eu-ai-act.
What does the team keep after the week? A working prototype that takes administrative load off your hands, a team with real AI competence and an internal AI literacy measures record. More on the process at /en/how-we-work.
