We help AI teams build, validate, and deploy skin analysis systems that perform accurately across melanin-rich skin tones.

Independent expert review of your model outputs to identify risk patterns, bias exposure, and performance gaps on deeper skin tones.

Structured, ethically sourced image datasets designed to strengthen training performance and improve model accuracy on melanin-rich skin.

Crystal Ngozi Ochemba is a licensed esthetician, medspa owner, educator, and founder of Melanoir Labs. With over a decade of hands-on clinical experience treating acne, hyperpigmentation, and barrier dysfunction in melanin-rich skin, she has worked at the intersection of real-world skin outcomes and emerging technology.
Inside her practice, Crystal began noticing a recurring pattern: digital skin analysis tools and AI-powered platforms frequently misclassified inflammation, post-inflammatory hyperpigmentation (PIH), texture irregularities, and undertone variation in deeper skin tones. These gaps weren’t always dramatic — but they were consistent.
Melanoir Labs was founded to address this problem at its root.
Crystal builds structured, ethically sourced datasets grounded in practitioner annotation and longitudinal client outcomes. Her work focuses on training data integrity, inflammation pattern recognition, and contextual interpretation of melanated skin — not just image collection.
In addition to dataset development, she serves as a Subject Matter Expert (SME) providing independent Skin AI Validation & Accuracy Reviews for early-stage and enterprise teams. She evaluates how AI systems present outputs to users, flags potential misclassification risks, and identifies confidence patterns that may misalign with professional skin assessment — particularly in melanin-rich populations.
Her work ensures that as beauty AI scales, bias does not scale with it.
Melanoir Labs reflects her core belief: skin technology should be built with clinical context, not assumptions.
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