Nitido: Glow Up Outfit Maker

One selfie reveals your personal colour palette. Nitido helps you check clothes before you buy and create outfits from the wardrobe you already own.

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Inspiration

I trained as a painter and decorator and earned my journeyman qualification. That work taught me how much colour can change what we see: the right combination can bring something to life, while the wrong one can make it look flat or unbalanced.

Later, while studying media informatics, I became fascinated by the technical side of colour. Cameras and screens capture and display it differently from how our eyes perceive it. Problems with lighting, exposure, and colour recognition connected directly with what I had learned in my trade.

My girlfriend's interest in fashion drew my attention to clothing. Many people do not know which colours suit them. They buy pieces that looked great in the shop but never feel right at home. Two items can look good separately yet clash together. Someone might receive compliments in one top and look tired in another without knowing why. You can buy clothes, but understanding what works for you takes more than buying another piece. That is why I started building Nitido.

What it does

Nitido turns a selfie into a personal colour analysis. It identifies where someone sits among twelve colour types and gives them a palette of thirty colours, plus six to avoid.

That result becomes a tool for everyday decisions. You can scan a garment before buying it and see how its colour fits your palette. You can save clothes in a digital wardrobe and get outfit suggestions from pieces you actually own, considering both whether they suit you and whether they work together.

How we built it

I built Nitido with React Native and Expo. The app takes three consecutive photos and measures the skin and iris to estimate warmth, depth, contrast, and clarity. It uses the whites of the eyes as a reference to help correct for lighting and camera exposure, then compares the measurements with twelve colour types.

Hair is deliberately left out of the result. It can be dyed, lightened by the sun, or appear different from one photo to the next. An in-person colour consultant often covers hair during an assessment for the same reason.

To assess garments, Nitido represents colours in CIE L*a*b* and compares them using CIEDE2000, an internationally standardised colour difference formula. I checked my implementation against published reference data. The app then uses the resulting palette to score garments, save them in a digital wardrobe, and suggest outfits whose colours work together. RevenueCat powers the subscriptions, while the selfie is processed on the phone and stays there.

Challenges we ran into

The hardest challenge was making a camera measurement dependable. The same face or garment can look different under daylight, a warm lamp, or a shop's lighting. Exposure and backgrounds add more variation. I had to account for those differences and recognise when a photo is unsuitable instead of presenting a confident-looking result anyway.

Another challenge was turning a technical colour score into advice someone can use. “This colour doesn't suit you” is less helpful than explaining whether it is too warm, too dark, or too bright, and helping the person make a better choice.

Accomplishments that we're proud of

I'm proud that I built an app that goes beyond naming a colour type. Nitido connects the analysis to shopping, the clothes already in your wardrobe, and complete outfits. I'm also proud that the facial analysis runs on the device and that the selfie does not need to be sent to an external service.

What we learned

My background taught me to take colour seriously, but building Nitido taught me how difficult it is to measure it consistently with a phone camera. I also learned that the result matters most when people understand why it helps them. A palette is useful; knowing what to do with it in a shop or in front of your wardrobe is better.

What's next for Nitido

I want to keep improving the measurement with more reference faces and real-world device testing, make garment colour capture more reliable, and refine how the app explains its recommendations. My goal is to bring together everything I've learned from painting, media informatics, and fashion to build the best personal colour app I can.

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