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Did IBM AI Invent Perfumes? What Philyra Actually Did

Philyra helped IBM Research, Symrise and O Boticário develop two fragrances that launched in Brazil in 2019. Human perfumers remained central to the process.
From TheFinanceBase Team4 min to read
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IBM did not independently invent or manufacture perfume. In a project announced in October 2018, IBM Research and fragrance maker Symrise used an AI system called Philyra to help develop two fragrances for Brazilian beauty company O Boticário. Human perfumers evaluated and refined the system’s suggestions; Symrise later said the products launched in Brazil in June 2019.

What happened—and when?

The headline was based on a real collaboration, but its “invented” wording compresses a team effort into a claim about a machine. Symrise said the Philyra project began in January 2018, and market-ready products were presented at the World Perfumery Congress in Nice that June. On October 24, 2018, Symrise announced the IBM Research collaboration and said two O Boticário fragrances were planned for 2019. Symrise later reported that the products went on sale in Brazil in June 2019.

The original story described O Boticário’s large retail network, not confirmed placement of both fragrances in every shop. Grupo Boticário’s current brand page describes O Boticário as having approximately 4,000 physical stores in 1,750 Brazilian cities; that present-day figure does not establish the exact number of outlets that stocked the launch products in 2019. Symrise’s account of the project and launch and its October 2018 announcement document the timeline; the current network figure appears on Grupo Boticário’s brand page.

What Philyra did

Philyra was an algorithmic assistant for fragrance formulation, not a robot that smelled, blended and released perfume on its own. It analyzed existing formulas and ingredient information alongside consumer and product data, then proposed candidate combinations for fragrance experts to assess. The aim was to search a large design space more quickly and help identify promising directions, including combinations a perfumer might not ordinarily try.

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Reported dataset sizes vary by source and date. Contemporary technical coverage cited a database of about 1.7 million fragrance formulas. Symrise’s later retrospective described a system based on more than 3.5 million formulas and approximately 1,500 raw materials. These are not necessarily contradictory: they may reflect different stages, versions or datasets. The sources do not establish one definitive figure that applies to every version of Philyra. See the 2018 technical overview and Symrise’s later account of Philyra.

Who made the fragrances?

Each organization had a different role: IBM Research contributed AI and product-design technology; Symrise brought fragrance expertise, materials and perfumers; and O Boticário was the commercial customer and brand owner. Symrise perfumer David Apel reviewed Philyra’s proposals and made adjustments, including emphasizing particular notes and improving how long the fragrance lasted on skin. That human evaluation mattered because a formula that looks promising in data still has to smell and perform well in real use.

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O Boticário’s 2018 sustainability report also describes its perfumers’ work with IBM and Symrise. It spells the system’s name “Phylira,” while Symrise uses “Philyra”; the available material indicates a spelling variation, not a separate system. The O Boticário report and Symrise’s announcement describe the collaboration and the perfumer’s role.

Which perfumes came out of the project?

Symrise investor materials identify the two products as Egeo ON You and Egeo ON Me, fragrances for O Boticário’s youth-oriented Egeo brand. The products were reported as launched in Brazil in June 2019; that historical report does not establish whether either fragrance is currently sold. The product names and Symrise’s reported consumer-test results appear in its 2019 investor presentation.

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What “AI-created” means in practice

In this project, AI-assisted creation meant using data to generate and explore candidates, followed by human judgment and ordinary product development. A simplified workflow looks like this:

  1. Set the brief: define the product type, intended market and target consumer.
  2. Search and model: analyze formula, ingredient, consumer-preference and performance data relevant to the brief.
  3. Generate candidates: propose combinations or modifications for consideration.
  4. Evaluate and refine: perfumers smell and assess candidates, then adjust them for balance, performance and the product’s intended use.
  5. Validate for sale: the chosen formula still needs appropriate safety and regulatory review, manufacturing validation and market approval.

Data can help with ingredient substitutions or changing consumer preferences, but it cannot remove practical constraints. A raw material may become restricted or unavailable in a market, and a substitute is not always a one-for-one swap. A pleasant formula intended for the wrong application may also fail: a scent suited to a candle or detergent does not automatically work as fine fragrance. Symrise discusses these development challenges in its account of fragrance work using data.

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Did the AI make a better perfume?

Symrise’s investor presentation said the AI-assisted fragrances scored higher than market fragrances for liking in consumer blind tests. That is a company-reported claim about particular products and a particular comparison. It does not show that AI generally outperforms human perfumers, prove that the perfumes would sell better, or establish how the test was conducted beyond what the presentation reports.

More broadly, a favorable test result is only one part of commercial success. Consumer response can vary by market, and sales also depend on factors such as branding, packaging, price and distribution. A computer-generated candidate is not a guarantee of a successful product.

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Why use AI if perfumers still make the decisions?

The potential advantage is augmentation rather than replacement. Searching many combinations can give a perfumer more options and help bring consumer or regional-preference data into the process earlier. Instead of treating a model’s output as a finished answer, a team can use it as a starting point, a prompt or a way to explore less familiar directions. Symrise described Philyra as a tool to accelerate creative design, and later reported using it in additional fragrance-development efforts; those descriptions are the company’s account of its own platform, not evidence that AI has become an industry-wide standard. See Symrise’s later update on the project.

The same approach has limits: models can reflect patterns in the data they are given, historical popularity is not the same as genuinely new appeal, and a candidate still needs sensory, technical and commercial judgment. The Philyra story is therefore less about a machine replacing a perfumer than about a new tool helping a human team search for ideas.

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