Govoy is a startup founded by three young experts not only in technology but also in disciplines such as physics and mathematics. In a country like Spain, where overqualification is a challenge, their case demonstrates that well-applied knowledge can make a difference. They have successfully translated their training into the creation of innovative and versatile solutions, particularly focused on logistics optimization, a key area for the productivity of any company with warehouses or distribution networks. Regarding their international presence, Govoy already operates outside of Spain thanks to national clients who also work in Portugal, allowing them to extend their activities to the neighboring country. Looking to the future, the company aims to expand its product catalog by integrating machine learning. Their goal is to evolve beyond transportation and delve into procurement, using predictive models to anticipate retailers' demand and help them manage stock accurately in each store. Concerning the artificial intelligence they employ, they distinguish between generative AI and machine learning. For the former, they rely on APIs from major tech companies like OpenAI, Google, or even Twitter, as the volume of data needed to train these models is unmanageable for small companies. However, in machine learning, they develop their own algorithms, as they require less data and their training is resource-viable. The development process begins by identifying the problem to be solved, whether it is prediction or description. For example, they have created a model capable of predicting whether a pharmacy will place an order with a distributor during a specific shift, achieving an accuracy of 91%. The process involves collecting and cleaning data, testing different models, and selecting the most effective ones while analyzing which variables are most relevant. They work with both internal (endogenous) data from clients and external (exogenous) information, such as weather conditions or disease indices, to enrich their models and enable them to learn autonomously. Regarding technological adoption in the Region of Murcia, they observe that large companies show greater awareness, while SMEs tend to focus more on day-to-day operations and have fewer resources to invest in innovation, although the long-term benefits are significant. Subsidies are essential for small companies to invest in digitalization, although access to these aids is complicated due to difficulties in locating them, the slowness of procedures, and the need to advance funds, which hinders many SMEs. Being a small company gives them agility and responsiveness: they can quickly redesign their solutions and launch products in just two weeks, something highly valued by large companies accustomed to slower and more bureaucratic processes. Regarding growth, they acknowledge that managing increasingly larger investments can be daunting, but they believe the real fear is not progressing. Initially, uncertainty was greater due to the lack of clients, but now the challenge is managing growth and new opportunities.