The biological intelligence behind better-informed decisions

Every patient’s biology is different, and better decisions start there. Mavatar helps researchers understand disease biology, helps pharma decide which drug candidates to back, and is developing tools to help doctors find the treatment most likely to work for each patient.

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Why now

The data exists, the intelligence layer doesn’t

Public biomedical data grows by millions of samples every year, and single-cell technologies now show disease at the level of individual cells. What’s missing is a way to turn that data into decisions people can trust. That’s what we build.

How it works

Traceable by design

Our proprietary DINA framework, grounded in more than 20 years of research at Linköping University and Karolinska Institutet and advanced with modern machine learning, builds digital disease models from over 8 million transcriptomes. Every output traces back to the data behind it. As AI moves into high-stakes decisions in drug development and healthcare, traceability is the standard it will be held to, and it’s built into our technology from the start.

One technology, three ways to grow

- Revenue today
- Our scientists answer the biology questions behind drug programs, before and during clinical development.
About Pharma Services
- Live today
- A research platform that puts our disease models directly in researchers’ hands.
About Discovery
- In development
- Clinical decision support for choosing the treatment most likely to work for each patient, starting in oncology. Designed to support clinical judgment, not replace it.
About Precision

Trusted by

Collaborators and partners

Precision medicine for IBD - RISE, Vinnova Funded
Partners: Pfizer, Takeda Pharma, Örebro Universitet, and Lunds Universitet delivering data-driven resources to identify molecular subtypes of IBD, explore predictors of treatment response, and more.
SOBI - IFNγ-driven mechanisms
Through advanced analytics of public transcriptomic data, we aim to contribute to a clearer understanding of IFNγ-driven mechanisms across relevant biological contexts.
Clínica Universidad de Navarra - Acute Myeloid Leukemia
A research collaboration with Bruno Paiva, one of Europe's most cited researchers in hematological neoplasms to validate candidate biomarkers and explore biological relationships in AML.
Lund University - Alzheimer's Disease
Together with Prof. Tomas Deierborg’s group, we explored galectin-3 and microglial activation in Alzheimer’s disease through brain and disease-specific networks.