Research & Technology
The science behind every GlamAR experience
GlamAR is built on purpose-trained AI — not off-the-shelf models. Every experience we ship is grounded in proprietary data, rigorous annotation, and continuous model improvement.
AI Facial Skin Analysis
Our approach to skin intelligence
Accurate skin analysis starts long before a model is trained. Our dedicated ML engineering team and AI researchers build from the ground up — starting with data quality, not model architecture.
Built on dermatology-informed annotation
Our dedicated annotation team labels skin images across diverse skin tones, ages, lighting conditions, and concern types. Acne, pigmentation, wrinkles, texture, dark circles — every concern is labeled with clinical precision before a single data point enters training.
The result is a model trained on over 5 million annotated images that performs consistently across skin types and real-world conditions — not just controlled environments.
The result is a model trained on over 5 million annotated images that performs consistently across skin types and real-world conditions — not just controlled environments.
14+
Skin concerns detected
24/7
Continuous model calibration
5M+
Annotated training images
Expert-labeled, pixel-verified
Every model update is measured against ground truth from trained annotators. Same image, three views: raw input, what an expert sees, what our model predicts
Acne
Scars
Whiteheads
Enlarged Pores
Acne
Scars
Whiteheads
Enlarged Pores
Acne
Scars
Whiteheads
Enlarged Pores
Virtual Try-on
Purpose-built models for every try-on category
Generic face meshes and off-the-shelf trackers break down in commerce. We train a separate model for each product category — optimized for real-time video, varied lighting, and the specific geometry each product demands.
Our ML engineers and AI researchers train category-specific models, optimized for video, not just static images, so tracking holds as the user moves in real time.
Eyewear
Face Tracking
Custom face landmark models trained for accurate frame placement across facial geometries — nose bridge, temple positioning, and scale.
Jewelry & Watches
Hand & Wrist Tracking
Real-time depth estimation and hand models trained for accurate ring, bracelet, and watch placement across hand shapes and skin tones.
Makeup
Lip & Eye Segmentation
Separate segmentation models per product category, trained to handle movement, shadows, and varied lighting conditions.
How we stay accurate
Research is not a one-time event
Our annotation team is not a historical input — they are an ongoing part of our model development cycle. As new products, categories, and use cases come online, the dataset grows. Models are continuously recalibrated against real-world performance data so accuracy improves over time.
1
Annotate
Label new data across categories
2
Train
Update models with expanded datasets
3
Deploy
Ship to production environments
4
Measure
Calibrate against real-world data
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