01 / 05

Telemyr started because we kept meeting the same company twice.

Different brands, different countries, different products, and the same problems sitting underneath all of them. We came at those problems from opposite directions and decided to work on them together.

02 / 05

The same problems, brand after brand

The pattern is consistent enough that we can usually describe a company's situation before they have finished explaining it.

The data exists but nobody trusts it, so meetings get spent arguing about numbers instead of deciding anything. Planning runs on experience and a spreadsheet that one person understands. Margin is known as an average for the business and not by product or market. Someone loses three days a month building a report that is out of date by the time it circulates. Growth arrives and the answer is another hire, because the process cannot absorb it any other way.

None of this is a failure of talent. These are good teams doing sensible things with the tools they were handed. The tools have changed and the way brands get supported has not caught up.

03 / 05

Rafael

Rafael has spent years running operations and technology inside consumer brands, most recently as CTO and COO of a venture that took investment from LVMH. Before that, aerospace and consumer goods.

The work has always been scaling. Technology, logistics, supply chain, planning, and analytics, in businesses growing fast and in global enterprises where nothing moves without process. He trained as an engineer and has stayed close to the detail of how teams, systems, and processes actually run day to day. That detail decides whether something works, and it is usually the part that gets skipped.

04 / 05

Fermin

Fermin holds a PhD in AI and came through robotics and engineering. He is a published scientist with contributions across autonomous systems, federated learning, and AI that runs on the device rather than in a data centre.

He brings frontier AI, data engineering, and machine learning to problems that are entirely ordinary. A demand forecast. A cost model. A production schedule. Most firms applying AI are applying what they have read about it. Fermin has built it, tested it, and published on it, and that shows up the moment a model meets real data instead of a clean benchmark.

What we want to build is a way for brands to grow while staying small.

Growth normally arrives as headcount. More orders means more people processing orders. More markets means more people managing markets. The cost base grows with the revenue and the margin ends up roughly where it started.

That is no longer the only option. Work that used to justify another hire can often be built once and then run on its own. What we are aiming at is a brand that can double without doubling anything else, with a team that comes out of it stronger rather than stretched.

05 / 05

That is why we started this.