AI research, skincare science, and company news
From the Tincyglo team
How we validate skin marker accuracy across skin tones
A look inside our bias-testing methodology and why per-marker accuracy matters more than a single blended score.
Hydration vs. oil: why your skin can be both dehydrated and oily
Breaking down a common skin-type misconception using scan data from 50,000 users.
Tincyglo raises Series A to expand clinical AI research
What the new funding means for our research team, our API, and our roadmap.
Inside our face landmark detection pipeline
How 68-point facial mapping normalizes every scan before analysis even begins.
The ingredient list red flags our AI catches most often
A breakdown of the top five ingredient conflicts flagged by our Ingredient Intelligence engine.
Introducing the Tincyglo API for clinics and brands
Our skin-scoring engine is now available to build on — here's what launched.
Why generative AI needs guardrails in skincare advice
How we constrain our AI Skin Coach to stay grounded in your actual scan data.
What a 'skin health score' actually measures
A transparent look at how eight markers combine into one number — and what it doesn't capture.
Tincyglo is now HIPAA-ready for clinical partners
A milestone update on our compliance roadmap and what it unlocks for hospital deployments.
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