Haut.AI's AI platform cuts skin study setup from months to days, scaling clinical trials 100x

For decades, measuring how skin changes has been a painstaking affair. A dermatologist looks at a face, makes a judgment, writes it down. Another dermatologist looks at the same face and writes something slightly different. A third examines it weeks later and the numbers shift again. This variability—the simple fact that human graders disagree with themselves and each other—has been baked into skincare research since the beginning, introducing noise that can obscure whether a product actually works.

Haut.AI, a clinical research software company, has built a platform designed to eliminate that problem at scale. The Clinical Studies Software uses AI models trained by dermatologists to measure 48 distinct skin biomarkers—wrinkles, pigmentation, texture, acne, redness, pore quality, and others—across the face, body, and hair. The consistency is remarkable. In validation testing conducted with a panel of expert graders at Institut d'Expertise Clinique, the AI achieved an intraclass correlation coefficient of 0.97 to 0.98 across five facial endpoints, a level considered excellent by clinical standards. More practically, it means the same skin receives the same score whether it's photographed by a professional with specialized equipment or captured by a participant on their phone at home.

That consistency unlocks something the beauty industry has never had: the ability to run large-scale skin studies remotely. Traditionally, a clinical efficacy trial enrolls 30 to 35 participants in a controlled lab environment, with trained graders present at every visit. The setup alone takes 8 to 16 weeks. Haut.AI's platform compresses that to two or three days—a reduction of more than 90 percent. Companies can now recruit hundreds or thousands of participants, have them submit images from home on a schedule, and get standardized measurements back automatically. The platform handles the full workflow: study design, participant management, image capture with quality assurance, AI measurement, cohort analytics, and generation of claims-ready reports.

The economics shift dramatically. In a traditional study, the largest costs repeat at every visit and every stage: clinical grading, site monitoring, operational management, data handling. The platform absorbs those costs through automation. Remote self-submission eliminates per-participant site visit expenses. AI scoring replaces manual grading. Scheduling and tracking happen in software. Because budget scales with recruitment rather than with headcount or visits, companies can run much larger studies for comparable total spend.

Anastasiia Georgievskaya, CEO and co-founder, frames this as an inflection point. "Clinical research in beauty and skincare has reached an inflection point," she said. "The industry has incredible expertise in clinical science, but the tools used to collect and analyse data have remained largely unchanged for years. Our goal is not to replace clinical studies. It's to enhance them by making skin measurable at scale." The implication is clear: teams that can measure continuously across larger populations don't just produce better science; they make faster decisions.

The platform is already in use. Deployments include a single consumer study that screened more than 7,000 participants for a global personal care company, yearlong longitudinal research programs run by Fortune 500 beauty manufacturers, formulation and ingredient evaluation for active ingredient suppliers, and standardized remote and hybrid workflows deployed across markets for a luxury beauty group. Varun Dwaraka, director of research at TruDiagnostic, described how the platform enabled his team to treat visible skin aging as a quantitative trait, pairing AI-derived measures of facial aging with DNA methylation data in ways that subjective grading never allowed.

Privacy is built into the design. Facial imagery is anonymized through Haut.AI's patented Skin Atlas technology, and personal and recruitment data remains with the research partner. The shift is subtle but consequential: the same technology that has constrained skin research for decades—human judgment, limited sample sizes, geographic friction, grader variability—is being replaced by something that scales without losing rigor. What was once a bottleneck in beauty R&D is becoming a throughput.