Munich-based Orbem, a
deeptech company applying artificial intelligence to magnetic resonance imaging
to analyse food and biological systems, has closed a €55.5 million Series B
financing round. The round was led by Innovation Industries, with participation
from Supernova Invest and follow-on investments from existing backers, including
General Catalyst, 83North, The Venture Collective, Possible Ventures, and a
group of angel investors.
Orbem applies
artificial intelligence to industrialise magnetic resonance imaging, enabling
fast, scalable, and non-invasive analysis of biological materials. Its platform
generates actionable insights from biological data to support decision-making
across agriculture, food production, and health.
The technology
provides access to information that was previously unavailable at an industrial
scale, such as determining embryo sex in eggs, evaluating seed viability, or
assessing produce quality without destructive testing. With proven applications
in the poultry industry, Orbem’s solutions help improve efficiency, reduce
waste, and increase transparency across biological value chains.
Its flagship product,
Genus Focus, uses AI-enabled MRI to determine the sex of a poultry egg
non-invasively in under one second, providing an alternative to the culling of
male chicks, a practice restricted in parts of the European Union. To date, the
system has been used to scan more than 170 million eggs, and the company is
expanding capacity to meet growing international demand.
Building on this
foundation, Orbem has introduced Genus Scale, a solution designed to assess egg
fertilisation status before incubation. This allows hatcheries to identify
non-viable eggs early, reduce inefficiencies, and redirect suitable eggs for
food use.
The Series B funding
will support Orbem’s expansion into the United States, further scaling of its
poultry solutions, and entry into the fruit and vegetable sector using
non-destructive quality analysis. The company will also continue developing
healthcare applications based on large-scale biological data.