Haut.AI skin recommendation tool finds unmet product demands

Haut.AI has released Deep CARE, using artificial intelligence (AI) technology to provide customized skin care recommendations to consumers. Frequent recommendations are then converted into insights for brands, providing oversight of portfolio gaps and unmet consumer demands.

Deep CARE (context-aware AI recommendation engine) builds on Haut.AI’s AI portfolio and expands beyond traditional skin categorization. The tool uses multidimensional product tagging and detailed skin profiling to generate personalized, science-backed suggestions. It then explains why the recommendations were made to phase out consumer skepticism about the technology’s efficacy. 

Personal Care Insights speaks with Anastasia Georgievskaya, CEO and co-founder of Haut.AI, about the system and how technology is evolving from being integrated into the industry to leading it.

Demand tallied

In addition to serving end users, Deep CARE provides beauty brands with insights into consumer demand and product gaps. By analyzing patterns in recommendation requests, brands can identify unmet skin care needs and adjust their development pipelines accordingly.

For instance, if users commonly flag under-eye concerns but the brand lacks targeted products in that area, the engine can highlight the demand.

Additionally, unlike static, rule-based engines, Haut.AI’s Deep CARE uses real-time updates. It automatically integrates new product launches and ingredient changes, ensuring that users receive the most up-to-date suggestions. Brands also benefit from a continuously refreshed inventory landscape.

Dynamic personalization

Diet, sleep patterns, stress levels, climate, and overall wellness goals impact the skin over time. Georgievskaya says understanding all these factors of a consumer’s life is crucial for accurate evaluations.

“AI can now connect multiple layers of information to build a more complete picture of the individual,” she explains.

AI reveals what is missing in brand portfolios.Georgievskaya expects that AI will continue to focus on personalization and building skin care routines tailored to each individual’s biology, lifestyle, and evolving needs.

“Personalization will no longer be static. It will become a living, adaptive process [that is] much closer to how human experts would approach long-term skin health. And Deep CARE is a step toward it.”

Logic behind the listings

AI-powered tools, while still relatively novel to the industry, are no longer seen as futuristic add-ons in their infancy. However, Georgievskaya explains that there is still a gap to be bridged for the technology to be trusted.

“This direct consumer feedback proves that people are no longer willing to trust AI blindly. They expect to see a logical connection between their identified concerns and the products suggested.”

A standout feature of the Deep CARE system is its explainability. It tells users why certain products are recommended, outlining the reasoning and linking it to the user’s inputs and concerns.

“We’ve consistently heard the same concern: Consumers are initially excited about AI but quickly become skeptical about the recommendations if they do not understand them. The most common question from users is, ‘Can you explain why this product was recommended to me?’”

While AI-driven skin care recommendation tools often group users under broad terms like “oily” or “dry,” this engine aims to distinguish itself by providing more specific context, such as “dry skin with hyperpigmentation” or “oily skin with rosacea.” It gives recommendations based on both product metadata and evolving skin data.

“This level of transparency makes AI-driven beauty recommendations feel more like a knowledgeable consultation with a specialist rather than a random or automated suggestion,” says Georgievskaya.

“What doesn’t work anymore is a black-box approach, where AI delivers an analysis and a set of products without communicating how one led to the other. If there is no visible bridge between the assessment and the recommendation, the experience feels generic, even if the underlying technology is sophisticated.”

Hovering hesitationDeep CARE links skin data to smarter product recommendations.

According to Georgievskaya, as long as consumers do not understand AI or the principles behind its decision-making, they will always hesitate to adopt it across all industries, not just beauty and personal care.

“We have to recognize that AI is still showing its flaws — whether it is hallucinations or inconsistencies in its outputs,” she says. 

AI hallucinations refer to content that the technology releases that appears to be accurate but contains dangerous assumptions or is factually incorrect. This type of content is usually made up by the tool rather than based on its training.

As long as these hallucinations exist, full trust will be difficult to achieve — “especially when it comes to personal topics like skin care and health,” she says.

With Deep CARE, transparency, clear explanations, and linking recommendations directly to a user’s inputs — showing them exactly why a product is suggested — are steps taken to make the technology adoption smoother.

“Trust is not built by making AI seem perfect; it is built by making AI understandable,” Georgievskaya concludes.