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We were teaching long before we developed software

We are teachers by vocation and by career. We teach technical engineering subjects at university and supervise master's theses in artificial intelligence. That capability is not an embellishment of our biography: it is part of the product.

What our training includes

A tool only transforms an organisation if the people understand it and make it their own. That is why training and support sit inside what we offer, not outside it.

  • Practical AI training

    Programmes for the staff of federations and clubs, centred on their real work: specific cases from their daily routine, not generic theory about artificial intelligence.

  • Support during adoption

    We do not hand over a tool and disappear: we support teams while they change how they work, until the solution is part of their routine.

  • Technical concepts turned into understandable processes

    Years of teaching have taught us to translate the complex: we turn technical concepts into processes that anyone in the organisation can understand and apply.

  • Master's thesis supervision that ends in prototypes

    We supervise master's theses based on application skeletons that students develop into working prototypes applicable to sport.

Universidad Europea

Teaching at the Universidad Europea

We work with the Universidad Europea on its Máster de Formación Permanente en Inteligencia Artificial Aplicada al Deporte (lifelong learning master's programme in Artificial Intelligence Applied to Sport): we supervise master's theses, give outreach talks and workshops and, from the 2026/27 academic year, we teach on the programme.

Universidad Europea

A real example

Injury prevention in women's football

A medical professional at a women's football club wants to set up an injury prevention tool adapted to factors specific to women's sport.

It is a project in preparation within our knowledge transfer work: an example of how academic supervision and engineering can turn a real need in sport into a working prototype. We describe it without names or sensitive data, and without presenting it as a completed implementation.

Let's talk about training