Before AJL had a name, I was already following the same impulse: notice a possibility, gather unusual people and make something that did not exist.
This is not a complete portfolio or CV. It is a selective, evolving map of ventures, products and experiments I created, co-created, led or helped bring closer to reality—and the line of learning that connects some of them.
“Success can be measured by how much you have learned.”
Each project changed my understanding of timing, technology, people or scale. Some became businesses. Some became products. Some were too early. None was wasted.
Before streaming became ordinary, I tried to make live media native to the internet.
Addhoc began with the idea of digital radio in Spain and moved into podcasts and online video. We streamed the first edition of Operación Triunfo when bandwidth was still an economic and technical constraint. It was early, difficult and formative: proof that timing, infrastructure and imagination have to arrive together.
Could the internet make serious medical and health knowledge understandable, useful and available to everyone?
After working in digital radio and streaming, I saw that health information would become one of the internet’s most important territories. I created VivirMejor.com as a medical and health-information portal for the general public: a place where specialist knowledge could leave the clinic and become accessible without losing its seriousness. We produced articles, reports, news and video interviews with doctors and other experts about prevention, wellbeing, medicine, illness and pharmacy. The project was not simply an online magazine. It was an early multimedia publishing system, connecting expert voices with people searching for understandable answers and distributing content through established media and specialist health channels. Building and managing VivirMejor.com also allowed me to see the emerging relationship between health, pharmaceutical communication, digital audiences and data. That insight became a bridge to later projects in healthcare advertising, product intelligence and artificial intelligence.
A visual underground for work that did not belong in conventional channels.
VUMP—Visual Undermedia Underground—was an exploration of image, moving culture and alternative distribution. I remember it as a space for visual ideas with more energy than category: part platform, part creative experiment, part refusal to wait for permission.
A production structure for turning stories, campaigns and ambitious ideas into finished work.
Lupama Producciones brought together the practical disciplines that make ideas visible: concept, production, coordination and delivery. It connected my interest in technology with the craft and pressure of making communication in the real world.
Helping organisations cross the gap between a technology promise and a system that actually works.
Prod Technology Consulting focused on the applied side of innovation: understanding the problem, choosing the right architecture and turning technology into something operational. The work strengthened a conviction I still carry—technology matters only when it changes what people can do.
Could music create a direct, scientifically observable connection before birth?
I co-created and patented BabyPod with Dr. Marisa López-Teijón: a small acoustic device designed to deliver music intravaginally so sound reaches the fetus with less attenuation than through the abdomen. The research produced surprising evidence of fetal response and was recognised with the 2017 Ig Nobel Prize in Obstetrics. Strange, rigorous and deeply human—exactly the kind of boundary-crossing project I love.
What if a machine could follow any product across the internet and turn a chaotic market into usable intelligence?
I founded Plyzer Technologies to understand how a product appears and changes across online channels. We built product matching, geolocation, machine learning and real-time market intelligence for brands and retailers. Plyzer became a transatlantic public company with its technology and team rooted in Barcelona—and one of my most ambitious lessons in building at scale.
Helping a Swiss deep-tech company move an ambitious X-ray detector technology closer to the market.
G-ray Switzerland was not an idea I created or a company I founded. I joined the journey as CEO, working with the team on the difficult passage from advanced scientific research to a viable technology business. Based in Neuchâtel, the company developed monolithic CMOS integrated pixel-array detectors and its latenium technology, with the ambition of making X-ray imaging sharper, safer and possible at lower radiation doses. Its potential applications reached from medical diagnostics and mammography to aerospace, automotive systems and high-energy physics. My contribution was on leadership, strategy, financing and commercialisation: helping connect a demanding semiconductor technology, research partnerships and the realities of bringing deep tech into the world.
A multidisciplinary AI lab built around finding the right intelligence for each problem.
Nennisiwok brought together predictive analytics, machine learning, natural-language systems, computer vision and automation. Its open-talent model assembled internal experts, external specialists and researchers around each challenge. The name means finding after searching—a useful description of both AI and invention.
Using AI to turn the world around a blind person into useful, navigable sound.
Near gathered context from maps, transit systems, public data, device sensors and computer vision, then translated the most relevant nearby information into audio. The project explored how a blind person could hear obstacles, landmarks, crossings, entrances and route decisions without losing the environmental sound needed to move safely. Its most important principle was simple: build with blind people, preserve the right to silence and make independence—not technological novelty—the outcome.
If AI participates in important decisions, how do I know when its output deserves trust?
Validix explores a central problem of the AI era: validation. It turns attention from impressive generation to evidence, provenance, consistency and the conditions under which an answer should be accepted. For me, it is part technology and part philosophy—a way to make trust inspectable rather than assumed.
Addhoc taught me about timing. Production taught me to finish. BabyPod taught me to trust a strange question. Plyzer taught me scale. G-ray taught me the demanding passage from science to a deep-tech business. Nennisiwok and Validix sharpened my thinking about intelligence, talent and trust. AJL is where those lessons meet the next idea.
See what I am imagining now ↗This is deliberately a selection, not a complete archive of everything I have done. It combines my own project history with surviving public material. Some early projects belong to a web that has largely disappeared; I describe those as I remember their purpose.