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Gübelin Gem Lab: Transparency and Pioneristic Spirit

President Raphael Gübelin introduces us to a familiar world that has been analyzing stones, and running a highly technological lab, since 1923

Who Raphael Gübelin, President
Where Lucerne, Switzerland
Why Gübelin combines tradition with innovation and the latest, digital, technologies, opening a new level of transparency and deep knowledge to clients and the entire industry
Web gubelingemlab.com

Since 2011 Raphael Gübelin has headed the House of Gübelin, founded in 1854. His focus on innovation, transparency and digitalisation follows the pioneering spirit that the Swiss, family-owned company stands for. The combinatio of businesses with the Gübelin Gem Lab to analyse gemstones, the Gübelin Academy to share passion online and offline as well as the Gübelin Jewellery creations inspired by the inner world of gemstones, is unique and mutually enhancing. The House of Gübelin has always been eager to gain a deeper understanding of its metier and share its insights with clients. The roots of the Gübelin Gem Lab reach back as early as 1923, when Gübelin began to manufacture its own jewellery. Since then, the laboratory has grown to one of the most respected institutions with sites in Lucerne, Hong Kong and New York, and is known for its expertise in coloured gemstones and origin determination. Provenance Proof, a separate entity of the House, currently offers two technologies enabling more transparency to the entire industry: the Emerald Paternity Test, tracing back emeralds to the mine by nanolables, and the Provenance Proof Blockchain, the first digital ledger for coloured gemstones to track their journey along the value chain. The Reference Stone Collection comprises more than 27,000 gemstones from all commercially relevant mines and is also the backbone of the Gemtelligence project. Gübelin Gem Lab has partnered with CSEM (Centre Suisse d' Electronique et de Microtechnique) to develop and transfer world-class data handling technologies based on Artificial Intelligence (AI) and deep learning using neural networks. This approach aims to increase the consistency and reliability of data interpretation, reduce potential human errors, and save time.

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