Glossary · Technology

AI Olfactive Design

AI olfactive design refers to the use of machine learning models and predictive algorithms to assist perfumers in selecting raw materials, optimizing formulas, and mapping olfactive families, without replacing human creative judgment (Givaudan Carto documentation, accessed 2026-05-27).

Technical detail

The most publicized AI perfumery tool is Carto, developed by Givaudan (2019), which allows perfumers to navigate formulas through a digital interface that maps ingredient relationships and predicts olfactive outcomes. Symrise developed Philyra, an AI trained on thousands of formulas used in the creation of Egeo Woman for O Boticário (2019) (Symrise press release, accessed 2026-05-27).

AI tools in perfumery operate on two main axes: formula optimization (replacing restricted or costly materials while preserving the olfactive profile) and trend prediction (analyzing consumer data to forecast preferred accords for new markets). In niche perfumery, AI adoption remains limited; most artisan houses prioritize the perfumer's unaided sensory intuition as a differentiator (Perfumer & Flavorist, accessed 2026-05-27).

Examples

  • Givaudan Carto (2019): first commercial AI perfumery tool used by in-house and external perfumers.
  • Symrise Philyra (2019): produced Egeo Woman for O Boticário, the first commercially released AI-designed fragrance.
  • IFF Scentrification: uses neural networks to translate non-olfactive briefs (images, music) into accord suggestions.

Sources

Published 2026-05-27 · Updated 2026-05-27 · Last fact check: 2026-05-27 · Osmetheca