AI skin analysis

Clinical-grade skin intelligence, from a single selfie

Fifteen seconds. Fifteen customer-visible scores, backed by 150+ biomarkers and 94 ML algorithms in-browser, no app install
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Trusted by global brands
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Backed by enterprise grade security and scale
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Les marques mondiales nous font confiance
Étude de cas
« C'était formidable de collaborer avec GLAmar et de travailler ensemble pour donner vie à cette expérience. »
Riveshu Trivedi
Produit AVP @ Foxtale
Étude de cas
La réalité augmentée va redéfinir la façon dont les bijoux sont vendus. Il élimine les conjectures, renforce la confiance et donne aux clients la clarté dont ils ont toujours rêvé. Pour moi, l'adoption précoce de la réalité augmentée n'était pas une question de technologie ; il s'agissait de garder une longueur d'avance sur l'évolution du marché.
Manan Sonchhatra
Fondateur et PDG de White Cut Diamonds
Étude de cas
« Le réalisme de l'expérience d'essai est très agréable, et le soutien de l'équipe de GLAmar a été excellent. »
Asadullah Salem,
Cofondateur de Low Cost Glasses

Scientific precision once reserved for dermatology labs,
now available in every browser

15

Customer visible scores graded in plain language every metric returns a 0–100 score, severity grade and annotated image

Acne Wrinkles Pores Eye conditions Pigmentation Hydration Redness
Skin care treatment close-up 150+

Skin biomarkers analysed for depth and detail in every scan

94

ML algorithms in the analysis pipeline graded against dermatologist annotated standards and retrained every six months

Clinical research dashboard and medical workspace 5M+

Dermatologist graded skin images used to train the AI across demographics, skin tones and conditions on six regions

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Condition Analysis of 14+ Skin Issues

Acne

Performs a comprehensive facial scan to detect whiteheads and inflamed pimples, returning an annotated image highlighting affected areas

Range Your Skin is Average
Skin Score:67
Skin Age:31
Analysed face

Customized product recommendations

Analysed face
Accurate Product Suggestions
Our AI-driven analysis offers personalized product recommendations for your customers' unique skin concerns
Better-Suited Alternatives
By analyzing ingredients and mapping them to skin concerns, we recommend alternatives that boost satisfaction and sales
For a Better Understanding, Talk to Us!
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Scan. Recommend. Build. Launch

Everything below ships as a drop-in SDK you can theme to your brand across web & mobile clinical scans, tunable recommendations, brand-built routines, custom UI, and shareable analysis reports
SkinGPT Online · knows your last scan
How is my skin doing?
Your Skin Reportscan 8F2A-67
Skin score67 / 100 · Average
T-zoneOily
Hydration66 · Needs attention
Can you show me where?
Annotated scan · tap a concern
Your analysed face
Want a fresh read? Scan right here.
What should I use for my concerns?
Matched to your scan add any to your routine:
Prices shown in-store · synced to your cart
Routine Builder
brand-configured in GlamAR · personalised by scan 8F2A-67
Your personalised routineEvery step, one tap from purchase
Clinical skin scan with annotated zones and issue labels
15 clinical skin scores with annotated zones across browser, chat, and in-store journeys
Skin report with shareable scan results
Run scans, view results,
and share reports
AM and PM skincare routine cards
Create AM/PM skincare routines from scan results.
Scanner UI customisation in light and dark themes
Customize the scanner with your brand colors, fonts, corners, and components
Ingredient mapping to skin concerns and product match
Match skin concerns to the right SKU with ingredient mapping

Measuring results with clinical relevance

Two clinically grounded scoring systems, paired with annotated concern masks so every result is not just a number, but a verifiable, explainable finding
Skin close-up grade 1
Good
Grade 1 Score 98/100 No visible wrinkles
Skin close-up grade 2
Good
Grade 2 Score 79/100 Fine, shallow barely noticeable wrinkles
Skin close-up grade 3
Average
Grade 3 Score 64/100 Prominent wrinkle that is deeper and wider with a visible base
Skin close-up grade 4
Needs Attention
Grade 4 Score 46/100 Deep, clearly defined wrinkle with raised borders on both sides
Skin close-up grade 5
Needs Attention
Grade 5 Score 27/100 Deep wrinkle with prominent raised borders, creating a carved appearance

The science behind the scan

Built like a clinical instrument, not a camera filter. A 94-model ensemble trained on multi-rater, dermatologist-consensus ground truth calibrated to dermatologist grading scales, fairness-audited across skin tones, and re-validated with every release
5M+

Dermatologist-consensus ground truth

Every image is labelled by a dedicated in-house team of dermatology-trained specialists, with multi-rater consensus and adjudication resolving disagreement. Ground truth is defined clinically not algorithmically guessed or weakly labelled

Multi-rater labelsConsensus adjudicationPixel-level masks
6

Fairness-audited across skin tones

A geographically diverse distribution stratified by age, gender and Fitzpatrick type, with per-subgroup bias audits and lighting-variation augmentation for domain robustness so accuracy holds from desert daylight to mall fluorescents, on any device

AsianEuropeanNorth AmericanMiddle EasternNorth AfricanSub-Saharan
94

Calibrated to clinical grading scales

A task-specific model ensemble EfficientNetB0 backbones with semantic-segmentation heads calibrated to dermatologist scales (Fitzpatrick, GAGS-style acne, Glogau photoaging). Probability calibration yields trustworthy confidence intervals on every read

EnsembleSegmentationCalibrated CIs
κ high

Dermatologist-grade agreement

Benchmarked against board-certified dermatologists using inter-rater agreement (Cohen's κ / ICC) and test retest reliability. The model grades within the spread of expert raters strong enough to support dermatologist review, triage and clinical-study feedback

Model card GlamAR PRO v3.1.0 May 2026
ArchitectureEfficientNetB0 backbone + segmentation heads
Ensemble94 task-specific models
Trainingtransfer learning scheduled continual retrain
Ground truthmulti-rater consensus adjudicated
Training set5M+ images 6 regions stratified
Robustnesslighting augmentation domain adaptation
Calibrationprobability-calibrated 95% CIs
ValidationCohen's κ / ICC vs dermatologists test retest
Fairnessper-subgroup bias audit Fitzpatrick I VI
Explainabilitypixel-level masks sub-area breakdown
Output per metricscore 0 100 severity grade mask CI version
Inferencein-browser ~15s end-to-end
What this research gives your brand
Dermatologist grade feedback loop

Outputs map to recognised dermatologist grading scales, so clinicians can review, correct and confirm. That human-in-the-loop feedback returns as active-learning signal the model keeps closing the gap to expert consensus

Reproducible R&D data

Probability calibrated, mask backed measurements with confidence intervals and model versioning reproducible enough to power efficacy claims, cohort studies and before/after product trials scan over scan

Trust at the point of sale

Explainable, dermatologist-aligned results customers can see on their own face the trust moment that converts a scan into a routine, and a routine into retention

Protecting identity with clinical integrity

Identity-removing anonymization strips facial identifiers while preserving every skin detail needed for analysis ensuring full privacy. The brand owns the data; no image ever trains outside models
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Built to run anywhere your customers are

From flagship mobile apps to in-store smart mirrors GlamAR Skin Analysis ships as native SDKs and plug-and-play platform connectors

Supported platforms

Native SDKs and deployment targets for apps, mirrors, and browser trials

Android

7.0+ (API 24), 10+ recommended

iOS

iOS 13+, native-only integration.

React Native

Hybrid apps with native camera modules.

Flutter

Android & iOS via InAppWebView.

Smart mirrors

USB or integrated cameras.

Web SDK

Browser-based proof-of-concept.

Platform docs

Backed by enterprise grade security and scale

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SOC 2
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GDPR
Go global with compliant AR solutions
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ISO 27001
Securely integrate AR with your systems

Frequently asked questions

Most platforms return a score and stop. This one returns fifteen plain-language scores backed by 150+ biomarkers, zone-level classification (a different read for your T-zone and U-zone), annotated concern masks, and a structured JSON payload the brand's systems can act on — all in-browser, in about fifteen seconds, with no app install.

Capture IQ runs three real-time gates before the analysis is ever invoked: a pose-landmark alignment check, an ambient light scan covering brightness and colour temperature, and a high-resolution 1-2-3 capture sequence. When conditions fail, the customer gets gentle corrective guidance — "move toward the window" — before the scan begins.

The models are trained on 5M+ skin images, dermatologist-graded and manually annotated by a dedicated in-house team. The set is geographically diverse — Asian, European, North American, Middle Eastern, North African and Sub-Saharan skin — and the model is retrained every six months on new data.

Every metric returns a 0–100 score, a severity grade against dermatologist-aligned thresholds, an annotated image of the exact concern region, a plain-language description, personalised analysis, and sub-area breakdowns — plus confidence intervals, model version and timestamp for the brand's downstream systems.

The brand does — contractually. Fynd is the processor, not the controller. Data is exportable in standard formats at any time and deletable on customer request. It never trains outside models, is never shared with other customers, and never feeds external benchmarks. Learn more about our data commitments.

Yes. The capture engine was trained explicitly with lighting-variation augmentation across real environments — desert daylight, mall fluorescents, evening selfies, humid climates — and the EfficientNetB0 backbone is tuned for accuracy across skin tones. The scan performs at the same level everywhere; it does not degrade across markets.

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1. What is an AI skin analysis?

An AI skin analysis is an advanced technology. It can analyze the live or uploaded images of your customers. Then, it will detect their skin concerns: wrinkles, dark spots, redness, pores, etc. Based on their skin issues, it will deliver personalized skincare routines and product recommendations. Thus, you can offer a quick and convenient way for consumers to get a skin assessment without a traditional dermatologist visit. 

2. How does an AI skin analysis work for beauty brands?

AI skin analysis for beauty brands uses computer vision to analyze your customers' photos. It can identify their skin conditions: wrinkles, spots, pores, redness, hydration levels, etc. Then, it will compare this data with a large database. As a result, consumers will get personalized product recommendations. It can create a unique skincare experience that can build customer trust with machine learning. Most importantly, it offers customized skincare routines for your customers and demonstrates potential product effectiveness. 

3. What are the benefits of AI skin analysis for skincare brands? 

AI skin analysis helps skincare brands offer personalized solutions. It studies your customer’s skin and gives accurate product recommendations. It helps them get appropriate skincare results. Skincare brands can also use this data to spot new trends and create suitable products. The technology tracks skin changes over time and shows real progress. It improves customer trust and satisfaction as people see real results. AI makes skincare effective with personalized skincare routines for consumers. It also helps your brand build customer loyalty through personalized experiences. Plus, your customers will obtain clear, helpful insights about their skin conditions.

4. Mention the challenges of integrating AI skin analysis for skincare brands.

Sometimes, integrating AI skin analysis may require high development costs. So, it can be difficult to connect with your brand's platforms. Handling sensitive personal data may raise privacy and security concerns. Also, AI systems can show bias if the data for training is not diverse enough. Most importantly, you need to follow strict data safety regulations to maintain compliance and protect your customers' information. As consumers need to feel safe sharing their data, building customer trust is another challenge. Finally, the technology should be easy to use so that customers can enjoy a smooth experience.

5. Which are the skin conditions that AI can detect?

AI can detect common skin issues of your customers: acne, eczema, and psoriasis. It can also help diagnose skin cancers: melanoma and basal cell carcinoma. AI is useful in spotting nail infections like onychomycosis and other inflammatory skin problems. Researchers are working to make AI detect complex or rare diseases. With more data and training, AI systems are becoming accurate and reliable in diagnosing a wide range of skin conditions quickly and effectively.

6. Is AI skin analysis accurate? 

AI skin analysis can give accurate outcomes if it has high-quality and diverse training data. The AI skin analysis studies thousands of skin images to learn how to detect different skin types and issues. In some cases, the training data includes different ages, genders, and skin tones. As a result, your customers will get reliable outcomes. It helps the AI detect their skin issues: acne, wrinkles, dark spots, redness, pores, etc. But its accuracy depends on good lighting, clear images, and advanced algorithms. It keeps providing accurate skin assessments for consumers and brands, with continuous updates and learning.

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