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A Munich startup has raised $32m for its revolutionary AI-based egg scanning

A Munich-based startup has secured $32 million in funding to advance its technology for egg scanning and has ambitious plans to extend its applications to human subjects.

Several European countries, including Germany, France, and Italy, introduced legislation last year to prohibit the culling of male day-old chicks, citing not only the wastefulness of the practice but also concerns for animal welfare and ethical considerations. These laws were enacted with the explicit goal of encouraging technology companies to devise methods for determining the gender of chicks before they hatch.

One technology that has emerged as a solution is hyperspectral imaging, which can accurately discern the gender of chicks based on plumage color on the 13th day of incubation. Commercial hatcheries are now employing fully automated systems like “CHEGGY” to implement this technology. These systems also have the capability to detect various characteristics of eggs, such as freshness, broken yolks, and shell cracks, with up to 97% accuracy in some cases.

However, there is a significant drawback to these systems: they operate slowly. In response to this challenge, Orbem, a startup based in Munich that originated from PhD research in MRI technology, embarked on a mission to expedite the egg-scanning process.

Orbem’s innovative solution combines an industrial MRI scanner with an AI-based platform, allowing hatcheries to determine the gender of an egg in a contactless and non-invasive manner. Orbem asserts that it can scan an egg in just one second, a significant improvement over existing methods. After scanning approximately 20 million eggs, Orbem claims to have achieved profitability.

Founded in 2019, the company recently secured €30 million ($31.8 million) in a Series A funding round led by 83North, with participation from new investor La Famiglia and existing investors The Venture Collective and Possible Ventures. Previously, Orbem had raised €10 million, with half of that amount coming from equity-free sources like research grants from the European Union and the German government. The founders of Orbem met during their PhD studies at the Technical University of Munich and established the company as a spin-off from the institution.

German AI startup ecosystem

In 2023, the landscape of AI startups in Germany has witnessed a significant surge, with 508 startups making their mark in the German AI Startup Landscape for the year. This marks an impressive 67% growth compared to the previous year. Of the 304 AI startups from 2022, 262 have retained their position on the list, while 246 new AI startups have secured a spot. Among the 42 AI startups that are absent from the German AI Startup Landscape for 2023, 28% have been acquired, 38% have been liquidated, 10% have relocated their products or companies, and 24% have been excluded due to having surpassed the 10-year mark. This highlights the notably high survival rate of AI startups in comparison to their non-AI counterparts.

Overall, industry observers believe that the substantial rise in the number of AI startups can be attributed to two primary factors:

First, it is worth noting that there is often a time gap between the actual establishment of an AI startup and its inclusion in the AI Startup Landscape. This discrepancy arises because only AI startups with robust business models and a certain number of full-time employees are listed (for more details, see the “Methodology” subsection). Consequently, there is a delay between the founding of an AI startup and its listing, as only the most promising startups make the cut. In contrast, AI startups with limited resources and unspecified value propositions are not included until they are further developed.

Second, the methodology for data collection has evolved and expanded, allowing for a more comprehensive view of the landscape. As such, the reasons for the remarkable growth can be attributed, in part, not only to the rapid expansion of the German AI startup scene but also to an improved data collection process.

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