AI-driven deciphering of genetic diseases

Our variant interpretation platform enables genetic testing labs to give more people with genetic diseases a clear and fast diagnosis.


Only about 30% of patients
receive a clear diagnosis.

Wright CF et cols. Nature Reviews Genetics (2018).
Posey JE et cols. Genetics in Medicine (2016).

Over 300 million people around the world live with a rare genetic disease. For most, the journey from first symptom to diagnosis lasts over 5 years, with many receiving incorrect or no diagnosis at all. Without diagnosis, patients remain without access to targeted medical interventions and potentially life-saving treatments.

New sequencing technologies are allowing millions of people to benefit from diagnostic genetic testing. However, the last step in genetic testing – variant interpretation – remains laborious and costly. A lab can spend hours, days or even weeks interpreting data from a single patient. Additionally, because of limited understanding of the consequences of variants throughout our genome, genetic test results are often inconclusive. Only about 30% of patients undergoing next-generation sequencing receive a diagnosis, leaving the majority without a clear answer.


Our platform leverages two
key innovations to solve this.

‍machine learning

Our ML model classifies and prioritizes variants in a way that is auditable and reproducible. Moreover, it learns from new data, but also benefits from expert knowledge: in addition to 30+ data sources, we modeled a decade of experience in clinical genetics.

synthetic biology

To solve more variants of unknown significance, the data we currently have is not enough. We solve this by generating new data in-house in a scalable manner with massive parallel experiments leveraging new approaches in synthetic biology.

Early Access

Variant Interpretation Platform

Our platform excels at variant interpretation thanks to our new technology and focus on what truly matters for you: accuracy and speed.
Automated variant classification (proprietary ML & ACMG guidelines)
The core technology enables a significant increase in diagnostic yield
Prioritizes variants according to HPO terms and suggests disease
Ensures full transparency and deep explanations for each result
Supports SNVs and indels in NGS panels, exomes, and genomes
Secure, encrypted and GDPR-compliant cloud platform with API

We are running an early access
program with selected labs.

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Our Team

David Gorgan

Co-Founder & CEO

Rocío Acuña Hidalgo, MD, PhD

Co-founder & CTO

David Neville, PhD

Head of machine learning

Gabriel Munteanu

SENior software

Carla Glassl

Biological data

Tina Xiong


Andrea Bertana, PhD

Data scientist


Scientific Advisors

Han Brunner, MD, PhD

Prof. of human genetics, Radboud medical center & head of clinical genetics, maastricht university medical center

Lea Starita, PhD

Assistant prof. at Department of Genome sciences, university of washington

Martin Kircher, PhD

Group leader in computational genome biology, berlin institute of health, charité berlin

Elgar Fleisch, PhD

Prof. of Information Management, ETH ZURICH & PROF. OF TECHNOLOGY MANAGEMENT, university of st. gallen


Join Us

Our team reflects the interdisciplinary collaboration required to solve this big challenge – ranging from software and data science to genetics and health care. We are a proudly diverse, international group of creative problem-solvers and humble learners that care about having a positive impact on society and are also aware of the trust placed in us. This is why we value transparency and kindness, taking ownership and we encourage your personal growth.
Open Application
BERLIN, Germany

We are backed by

Entrepreneur First
Venture capital
federal grant
AWS Activate
Cloud Resources