About us

We were born inside the hospital, not outside it.

A team that works inside the hospital, not above it. Here is where we come from and where to find us.

Our story

Latrikos starts from a question that came up in every session: if the data is already written, why does it take months to assemble?

We build alongside care and research teams so the tool fits how they already work, not the other way round.

The same rule still holds: nothing ships unless it withstands clinical and methodological review.

Find us

Map of central Madrid, with the area where Latrikos is based highlighted. — Central Madrid

Headquarters: Madrid, Spain
Write to us: contact@latrikos.com

Raw clinical documentation rises through the AI core into structured, protected, customer-owned data.

Why Latrikos exists.

Mission

To make real clinical information convertible into useful evidence, without healthcare teams having to choose between depth, speed and control of their data.

Vision

A health system where every hospital, scientific society and sponsor can activate its clinical data quickly, build reusable longitudinal registries and collaborate on multicentre studies without depending on inaccessible infrastructure or years of integration.

Latrikos was born out of clinical practice and research. We know the cost of reviewing reports one by one, the difficulty of reconstructing a longitudinal history and the frustration of designing studies that do not scale. That is why we built a platform combining clinical knowledge, AI, human validation and an architecture oriented to data sovereignty.

Six brand principles.

Clinical first

Variables, rules and validations are designed from clinical meaning, not from the technical availability of the data.

Evidence over hype

We do not promise automation without control. We measure quality, show traceability and adapt the flow to the intended use.

Data sovereignty

The asset belongs to the client. Technology should increase their capability, not turn them into a third party’s data supplier.

Operational impact

The goal is not to produce an AI demo, but to cut times, activate studies and deliver usable results.

Scalable collaboration

Models must be able to grow across services, hospitals, countries and projects while keeping a shared semantics.

Reproducibility

Every project should leave behind a methodology, a model and a registry that can be reviewed and reused.