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AI should adapt to your organisation. Never the other way round.

The people who think your transformation through are also the people who build it.

Teams put together for each assignment, combining strategic thinking, engineering and research.

Tailor-made teams.

For each assignment, we bring together the skills it needs: engineering, data, expertise in the business areas concerned. To do so, we rely on a network of reliable, well-established partners we have worked with for a long time. Together, we form operational teams that we lead ourselves. If you prefer, we can also lead your own teams, or those of your supplier. Either way, you keep the same point of contact from the first meeting to the end of the assignment.

Philippe Buschini

CEO & CTO

Researcher, then chief technology officer, then company director, Philippe Buschini has been working on artificial intelligence since long before the generative wave. He designs AI technologies applied to data, with one constant requirement: to draw value from them without giving anything away on privacy. This threefold background allows him to innovate, to think through a strategy knowing what will have to be built, and to build knowing what it must serve. At U-CIE, he brings these two trades together: thinking the transformation through, and building it.

Alain Zerzuben

CFO

A board member of a Zurich bank, where he is responsible for audit, credit, compliance and risk control, Alain Zerzuben knows finance from the inside. Head of a management consultancy, chairman of a fund and a board member of several foundations, he has set up banking systems, structured companies and funds, and led many projects. At U-CIE, he makes sure every transformation also delivers on its promises in figures: real costs, controlled risks, demonstrated compliance.

Yotam Igal

AI Architect

An artificial intelligence architect, Yotam Igal designs the systems that take AI from idea to production: language models, multi-agent systems, integration into business processes. His standard: an architecture that fits into the company’s real flows, that the teams understand, and that makes it possible to change models without rebuilding everything. At U-CIE, he turns the choices made with you into systems that work, and that remain under your control.

Brice Ruster

Data Scientist

A data scientist, Brice Ruster makes data speak to inform decisions: statistics, machine learning, predictive modelling. He starts from the questions the company is asking, not from the data available, and looks first for the data that really matters. Rigorous and pragmatic, he builds models that help anticipate, and indicators that show, over time, whether they deliver on their promises. At U-CIE, he makes sure every use of AI rests on solid data and measured results.

Michael Chemla

AI Enablement Lead

As AI Enablement Lead, Michael Chemla makes sure AI is adopted by the people who will use it, not just deployed. Working between business and technical teams, he identifies the uses that matter, supports their roll-out and trains staff starting from their own work. A patient teacher and a pragmatist, he involves teams from the design stage, because a tool designed without them almost always ends up being worked around. At U-CIE, he makes sure your teams understand their tools well enough to stay in control.

Théo Senecal

AI Product Manager

As AI Product Manager, Théo Senecal bridges what teams need and what AI can really do. Working alongside the architects and data scientists, he helps turn business objectives into precise uses, prepares the prototypes and follows how they are tested with users. Curious and methodical, he keeps a close eye on the fast-moving world of AI tools. At U-CIE, he makes sure every use meets a real need, not an appetite for technology.

We recommend nothing we could not make work.

We were practising AI long before it became fashionable.

Generative AI has taken over conversations in just a few years. Our culture of artificial intelligence and engineering goes back much further. That changes a great deal: we know where these technologies come from, what they do well, and what they are wrongly credited with. This depth does not take us away from the ground. It is what allows us to ask the simple questions nobody dares to ask, and to explain the choices that matter without jargon.

  • Artificial intelligence engineering

    We build AI systems, from choosing the models to going live. That is what makes our advice realistic, and our commitments achievable.

    Every component is chosen for its relevance, its reliability and its ability to evolve. Not because it is fashionable, nor out of habit. The tool adapts to your company, never the other way round. And tailor-made, for us, does not mean reinventing everything: we keep what works, and we only develop what really needs to be developed.

  • Research and innovation in artificial intelligence

    We come from research, and it shows in the way we work. Faced with a problem, we form hypotheses, test them, look for where the technologies reach their limits, and say so. When existing answers are not enough, we build others. That is how the AI Flow Map was born, on the method side.

For us, research is not a shop window. It is a workshop.

Our current research →

Five commitments, kept assignment after assignment.

Our expertise is not measured by how complex our language is, but by our ability to make the most complex subjects understandable.

  • Ethics

    It is in the DNA of the way we work. We see AI as a means of progress, in the service of people. We refuse to deploy a system when nobody can say who is responsible for it, or how far it is allowed to go on its own.

  • Confidentiality

    Your data, your sensitive information and your know-how are protected by design, not as an afterthought. Nothing goes anywhere without you knowing where, why and on what terms.

  • Architectural sovereignty

    We defend your freedom of technological choice. Open or proprietary models, hosting on your premises or in the cloud: we have no dogma, but we refuse to lock you in. Every dependency must be chosen, known and, as far as possible, reversible.

  • Transparency

    We explain what we do, why, and where the limits are. We would rather convince you than ask you to take our word for it. And when AI is not the right answer, we tell you so, even if that ends the assignment.

  • Passing on knowledge

    We speak plainly, and we pass on what we know. Our aim: for your teams to understand their systems well enough to decide without us, and to still know how to work without them.

Let’s talk about your organisation and your needs. Not about technology.

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