Something quiet but consequential is changing the way consumers find beauty products. Shoppers are no longer relying solely on search bars and category filters. They are asking ChatGPT which serum is best for sensitive skin. They are getting ingredient summaries from Google AI Overviews before they ever land on a product page. They are using retailer AI assistants to narrow down a foundation shade across dozens of brands in seconds. For beauty retailers, this shift raises an urgent question: when an AI system decides which products to surface, what determines whether yours makes the list?
Estella Benz, Founder and CEO of Inference Beauty, has spent years building the answer to that question into infrastructure. Her company, formerly known as Skin Match Technology and rebranded to Inference Beauty in 2025, provides beauty retailers and brands with the structured product and ingredient data that powers smarter product discovery, more accurate AI-driven recommendations, full product transparency, and personalized experiences. In Benz’s view, the brands winning in AI-powered discovery are not necessarily the ones with the biggest marketing budgets. They are the ones with the cleanest, most structured, most transparent data.
Why AI Surfaces Some Brands and Not Others
The mechanics of AI-powered discovery are less mysterious than they appear. Platforms like ChatGPT, Google AI Overviews, and on-site retail AI assistants all rely on the same fundamental input: structured, verifiable product and ingredient information. When a shopper asks an AI tool to recommend a fragrance-free moisturizer for combination skin, the system can only return accurate results if the products in its knowledge base have been tagged with precise, standardized attributes. Vague claims, inconsistent ingredient naming, and shallow product descriptions do not give AI systems enough to work with.
“Retailers that have invested in clear, science-backed ingredient communication and structured product data are far better positioned to be surfaced by AI systems,” said Estella Benz, Founder and CEO of Inference Beauty. “It is not about gaming an algorithm. It is about giving AI the information it needs to make an accurate match. The e-commerce stores that do this well will increasingly have a structural advantage in how they are discovered.“
This is where most beauty retailers are currently falling short. Product information is often too inconsistent, too shallow, or too manually maintained to support the demands of modern AI-driven shopping journeys. Ingredient lists are present but rarely decoded. Benefit claims are marketing-driven rather than science-backed. Product attributes vary across brands within the same catalogue, making cross-category filtering unreliable. The result is that AI systems, whether on-site or external, are working with incomplete information and returning imprecise recommendations.
The Role of Ingredient Transparency in AI Visibility
Ingredient transparency has moved well beyond a consumer wellness trend. It is now a structural requirement for AI discoverability. When a shopper queries an AI platform about products suitable for their skin sensitivity, the system needs ingredient-level data to generate a trustworthy answer. Brands and retailers whose ingredient information is standardized, validated, and enriched with functional context are simply more likely to appear in those results.
Inference Beauty addresses this through a proprietary database of more than 60,000 standardized cosmetic ingredients, each categorized by source, function, effect, and utility. The company’s Ingredient Intelligence and Transparency Engine translates complex INCI terminology into clear, consumer-friendly insights while making that data actionable for product search, filtering, personalized recommendations, and AI-generated responses. This combination of regulatory readiness and consumer-facing clarity is increasingly what separates discoverable products from invisible ones.
“Our database is not built from user-generated content or simple web crawling,” said Benz. “It is validated, structured, and connected to our proprietary ingredient intelligence. That gives us a much stronger foundation for accuracy and consistency, and it means the data we provide to retailers actually performs under the scrutiny of AI systems that are checking for reliability.“
Regulatory pressure is reinforcing this direction. From July 2026, the updated INCI Glossary under EU Commission Implementing Decision 2025/1175 becomes mandatory for all products placed on the EU market, requiring standardized ingredient naming across all digital retail environments. Canada’s SOR/2024-63 framework introduces parallel requirements for products sold online. Retailers who have already structured their ingredient data to meet these standards are arriving at compliance with a commercial advantage already in place.
Structured Data as a Conversion Tool
The benefits of structured product data extend well beyond AI visibility. When product information is clean, consistent, and ingredient-deep, it improves every layer of the digital shopping experience. Search results become more relevant. Filters become more useful. Product detail pages carry more of the information shoppers need to make confident purchase decisions. The path from discovery to conversion shortens.
Inference Beauty’s Beauty Product Data Enrichment and Catalog Structuring capability transforms raw product and ingredient information into structured, retailer-ready data that supports smarter search, benefit-based filtering, clean beauty validation, concern-based discovery, and more advanced merchandising logic. Retailers deploying this infrastructure alongside Inference Beauty’s full platform, which includes AI Skin Analysis for Skincare Personalization, AI Hair Analysis for Haircare Recommendations, AI Fragrance Finder with Olfactory Matching, AI Foundation Shade Matching Across Brands, and a PDP Personalization and Recommendation Engine, report a 30% increase in conversion rate, a 30% increase in average basket value, and a 10% increase in products added per basket.
“The retailers seeing the strongest results are the ones who treat product data as a strategic asset rather than a back-end maintenance task,” said Benz. “When your data is structured and ingredient-deep, it does not just improve your AI recommendations. It improves every touchpoint in the shopper journey.“
What Beauty Retail Looks Like When the Data Is Right
Inference Beauty currently serves more than 100 retailer and brand integrations across the European Union, United States, Canada, Australia, and the United Arab Emirates. The company is an alumnus of the Beauty Tech Atelier L’Oréal Accelerator and a winner of the ForwardBeauty Challenge by Douglas, recognitions that reflect both the technical depth and commercial relevance of its approach.
Benz’s long-term vision is for Inference Beauty to become the core intelligence layer for beauty commerce globally, where structured product data, ingredient knowledge, and AI personalization work together to create the most trusted and personalized beauty shopping experience available online. In the near term, the opportunity she is focused on is more immediate: helping retailers build the data foundation that makes everything else possible.
As research on AI-driven beauty brand visibility has noted, the brands most likely to benefit from the rise of AI-powered discovery are those that have already done the work of structuring and validating their product information. The window to build that advantage is open now, but it will not stay open indefinitely.
“The shift toward AI-powered product discovery is not coming. It is already here,” said Benz. “The retailers and brands that invest in structured, transparent, ingredient-level data today are building an advantage that will compound over time. Those that do not will find themselves increasingly invisible in the channels where modern shoppers are making their decisions.”
