Marat is a Master Dental Technician, international educator, and the developer of MATISSE, an innovative shade-matching software designed to improve color communication and predictability in dental restorations.
Born in Tbilisi, Georgia, in 1988 to Armenian parents, Marat moved to the Netherlands in 1993, where he grew up and began his journey in dental technology. During his education, he received the award for Best Thesis for “Key to the Future,” a project exploring the role of psychology in dental technology. At just 21 years old, he qualified as a Master Dental Technician and joined Natuurlijk! Tandtechniek in Veenendaal, the Netherlands.
In 2012, Marat further expanded his expertise by training as a Clinical Dental Technician. He subsequently became manager and co-owner of the laboratory, a position he held until 2020. Throughout his career, his commitment to continuous learning has taken him around the world, connecting with leading professionals in dentistry and exploring new approaches to dental aesthetics, color science, and interdisciplinary collaboration. His conversations with influential figures in the field have also been featured in Labline Magazine.
In 2019, Marat developed MATISSE, driven by the ambition to make shade matching more systematic, accurate, and predictable for dental professionals. What began as an innovative approach to color communication continues to evolve, supporting a deeper understanding of natural tooth color and its successful reproduction.
Today, Marat shares his knowledge internationally through lectures and hands-on courses, combining scientific principles, practical techniques, and real-world clinical applications. His teaching focuses on helping dental professionals understand color, interpret natural tooth characteristics, and translate these observations into highly esthetic, lifelike restorations.
Through MATISSE and his educational work, Marat continues to contribute to the advancement of dental color science, empowering dental technicians and clinicians to approach shade matching with greater confidence, precision, and predictability.