Lynceus allows you to predict the outcome of high value manufacturing processes, reducing scrap and increasing production capacity.
Quality defects plague factories’ P&Ls. Manufacturers lose 20% of their revenue to them because they can’t anticipate defects, and are limited to damage control. This is particularly prevalent in the semiconductor manufacturing process where demands are increasing every year and production can’t keep up. Higher yields are desperately needed, but quality issues drain vital capacity.
Lynceus offers a new approach, centred on reliability and actionability. Bridging the gap between isolated science experiments and real value on your production floor, they provide software with edge/cloud hybrid architecture, deployed within manufacturing process control tools. Supported by the most robust machine learning models, they are building a scalable generic product. Today, their platform leverages manufacturing data and in-house process comprehension to predict the performance of manufactured goods and systems.
Prior to Lynceus, David held various Ops leadership positions in companies like Uber and Circ (acquired by Bird), and advised investment funds on the acquisition of industrial companies. David holds an MSc from ESSEC Business School.
Guglielmo has a Master’s in physics and a PhD and postdoc in computer science, with a focus in Neural Networks. His post-doc was focused on transfer learning techniques for deep neural networks and one of his architectures was reused by Google DeepMind. Guglielmo previously launched another company in the AI space for semiconductor manufacturing.
Founded at Entrepreneur First
Seed round raised