AI expertisewith clinicalunderstanding.

Two disciplines, one team.

Healthcare AI fails when it is built by one side talking past the other. We sit in the overlap — clinical workflows on one axis, on-prem AI engineering on the other — and we build for both at once.

"The hard part of healthcare AI is not the model. It is the rest of the work."

Identify, build, deploy, iterate.

We start with a real hospital problem, build a targeted system, deploy it inside the hospital, and iterate based on usage. The loop closes with usage, not with a release note.

Josip Vrdoljak

Josip leads the AI in Biomedicine Laboratory at the Faculty of Medicine in Split — a role that reflects his rare combination of medical training and AI engineering. That dual perspective gives him direct, firsthand insight into where healthcare processes break down, and what it actually takes to fix them with AI.

Domjan Barić

Ten years of building AI systems and leading engineering teams across multiple companies. His PhD research in biophysics focused on interpretable AI for time series analysis — work that directly informs how Discens Machina approaches clinical data. Author of several scientific papers in medical AI.

Bojan Islamović

Years of building digital solutions that simplify day-to-day operations for companies of all sizes. He has worked with corporations and small businesses alike, and is most focused on how technology, AI, and better processes can reduce chaos and make people's daily work easier. He doesn't start from the technology — he starts from the real business problem: where time is lost, where the process stalls, and how to build a solution the company will actually use, not just install.