Manukai secures $3 million pre-seed funding

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31.03.2025
Cofounders L-R: Nicolas Vetsch, Daniel Wälchli & Pascal Weber
ETH spin-off Manukai has raised $3 million in an oversubscribed pre-seed funding round led by QBIT Capital, marking a significant milestone in its mission to create proprietary AI models that unlock the value of data in manufacturing. The startup has already gained significant traction with industry leaders like Bosch. The fresh funding will be used to refine its product, aiming to make Computer Numerical Control (CNC) machining more efficient.

High-precision metal parts are essential to manufacturing, yet the CNC machines that produce these parts need to be programmed manually. With increasingly scarce skilled workers—metal machine setters were the top 5 occupation in demand in 2023 in Europe—this bottleneck is intensified by geopolitical tensions, price pressures, and the shift toward regional supply chains. Manukai was founded in December 2023 and develops AI technology to solve this bottleneck.  

One of the main challenges in AI adoption in CNC machining are the stringent privacy requirements and established workflows. Manukai's patent-pending solution uniquely addresses the challenges by allowing their clients to leverage historical data on-premise. Their software package seamlessly integrates with existing CNC programming tools and deliver significant value without workflow disruption: manufacturers can accelerate programming, enhance machining efficiency, and centralise institutional knowledge while continue using their familiar tools.  

“What truly sets us apart is our data-driven approach,” explains Pascal Weber, CEO and Co-Founder of Manukai. “Our algorithms leverage client data within their secured environments, ensuring total IP protection while enhancing CNC programmers. Rather than attempting to replace human knowledge, we amplify it by easing access to past programming strategies. This approach respects that the worker’s experience is the gold standard—we simply help them access and apply that knowledge more efficiently.” 

Funding to fast-track AI-powered manufacturing innovation and market entry  
The funding round of $3 million (CHF 2.645 million) was led by Swiss VC QBIT Capital, with participation from Bloomhaus Ventures, and a group of angel investors. Funding from Innosuisse is also included in the announced amount. The funds will be used to accelerate the development of Manukai’s patent-pending AI technology towards a market-ready product.  

“Manukai exemplifies how AI can unlock immense value from legacy data, revolutionizing manufacturing efficiency worldwide,” says Georges Khneysser Founder and GP of QBIT Capital. “With Switzerland's unmatched industrial heritage and Zürich's cutting-edge AI ecosystem, Manukai is poised to lead the global evolution of manufacturing.” 

Pascal Stürchler, Co-Founder and CEO of Bloomhaus Ventures, adds: “Manukai is tackling one of the most pressing challenges in manufacturing with a groundbreaking AI solution. By removing key inefficiencies, they are unlocking new levels of productivity and competitiveness for manufacturers worldwide. Their strong founding team, combined with a scalable and industry-agnostic approach, positions them for lasting success. We are excited to support their journey and see them drive real change in the industry.” 

New co-founder for Manukai 
The co-founders Pascal Weber and Daniel Wälchli both hold a doctoral degree from the Chair of Computational Sciences (CSElab) at ETH Zürich and Harvard University. Their extensive knowledge to apply the latest methods for AI to engineering and the support from Torbjørn Netland, Co-Founder at Ethon AI and Professor at the Chair of Production and Operation Management at ETH Zürich drives the creation of a novel software products for manufacturing. They are currently welcoming Nicolas Vetsch as a late co-founder, bringing extensive expertise in scaling organizations and leading marketing and sales initiatives. 

(Press release) 
Image L-R:
Nicolas Vetsch, Daniel Wälchli & Pascal Weber

 

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