Engineering for Skin Analysis
Most skincare apps are glorified photo filters. We built a computer vision pipeline that measures what matters—with engineering-grade precision.
Our Tech Stack
Built from scratch with PyTorch. Zero reliance on third-party APIs. Complete data sovereignty.
Custom Neural Networks
Proprietary PyTorch models trained on diverse datasets. We don't use off-the-shelf APIs that compromise your privacy or accuracy.
On-Device Processing
Your photos never leave your phone during analysis. All inference runs locally using CoreML optimization.
Privacy-First Architecture
End-to-end encryption. Anonymized data handling. You own your skin data, not us.
Continuous Improvement
Our models evolve. Regular updates based on aggregated, anonymized data improve accuracy for everyone.
The Analysis Pipeline
From selfie to insights in seconds. Here's what happens under the hood.
Image Preprocessing
Face detection and alignment using landmark detection. Color normalization to account for lighting variations. Region of interest extraction for targeted analysis.
Feature Extraction
Convolutional neural networks extract high-dimensional features. Attention mechanisms focus on relevant regions. Multi-scale analysis from texture to structure.
Multi-Task Prediction
Simultaneous inference across 10 skin metrics. Shared representations improve efficiency. Independent heads for each pillar ensure specialized accuracy.
Score Aggregation
Weighted scoring based on relative importance. Temporal smoothing reduces noise. Confidence intervals for transparent uncertainty quantification.
Insight Generation
Trend analysis over your history. Correlation with routine data. Personalized recommendations based on your unique patterns.
Built on Real Data
Our neural network is trained using a combination of synthetic data and curated real-world datasets, with severity rankings informed by input from a board-certified dermatologist.
Unlike consumer apps that rely on subjective beauty standards, our reference labels are grounded in structured evaluation criteria. A dermatologist reviews and ranks sample images across key skin metrics—including acne, redness, hydration, aging signs, and eye vitality—helping guide our algorithm development.
We continuously analyze model performance across different skin tones, ages, and visual characteristics to improve robustness and reduce bias. Our goal is transparency and consistency: measurements based on observable features, not diagnosis or subjective ideals.
Experience the difference.
Join users who track their skin with engineering precision.
