Contact Us | MediXtract - AI-Powered Patient Data Abstraction

Tomás González

CO-FOUNDER · LEAD DEVELOPER

Tomás brings a biology major's understanding of clinical context to the engineering challenges of medical data extraction. His background ensures that the systems we build reflect how biological and medical knowledge is actually structured, documented, and interpreted in practice. He leads extraction engine development, the design of clinical decision trees, and the ongoing collaboration with clinical teams.

Joan Escat

CO-FOUNDER · LEAD ENGINEER

Joan's background spans philosophy and mathematical engineering. His expertise drives the architecture of the consistency testing framework, the data governance pipeline, and the design of the evaluation systems that make MediXtract's quality standards verifiable and transparent.

What Defines Us

We build intelligent data collection systems and evaluate their performance at scale.

Clinical Grounding

Every product decision is shaped by people who understand the clinical context. We build with the institutions and experts who live the problems we're solving.

Radical Transparency

Our AI systems are never black boxes. Every extraction is traceable to its source. Every performance metric is visible. Institutions always know exactly how their data is being processed.

Responsible Innovation

We operate under the principles of Responsible Research and Innovation — involving the right stakeholders early, evaluating impact honestly, and never prioritising speed over safety.

Our Mission

"Transforming unstructured clinical data into the foundation of tomorrow's medical research."

MediXtract was founded on the conviction that healthcare institutions can unlock vast amounts of underutilised clinical knowledge through technology developed in genuine partnership with clinicians. We are dedicated to making healthcare data actionable for science, operating at the boundary where technology and medicine meet.

Origins

We are two friends from secondary school who were captivated by the world of AI after we started watching Dot CSV's YouTube videos back in 2018. We witnessed the first tests of GPT-1 (well before the fame of ChatGPT) and were awestruck when early image generators would produce a plain green image when you requested a forest—it was mind-blowing to think that an AI could grasp that a forest is green.

Joan chose his two separate degrees in Philosophy and Computational Mathematics with the vision of joining the LLM wave before ChatGPT even existed and Tomás developed his own automatic AI notes app during his university years before NotebookLM was released.

After years of studying and working on multiple independent programming projects, a key connection gave us the opportunity to bring our knowledge together to transform the way clinical data is recorded and managed.

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