What it does
Palette is a mobile app designed to help users discover their personal color palette through AI analysis. By uploading a single portrait photo, the app identifies the user's seasonal color type, such as 'Warm Autumn'. It then provides a comprehensive guide to the colors that best suit them, broken down into categories like neutrals, best colors, lip colors, nail colors, and general style tips.
Where it shines
Palette excels in transforming a potentially complex analysis into a simple, actionable guide. The onboarding process, particularly the 'Do's & Don'ts' screen at 00:25, is a highlight. It smartly educates users on how to provide the best possible photo, ensuring the AI has quality data to work with. The true strength of the app, however, is revealed post-analysis. Instead of a static report, users get an interactive, swipeable result (01:16) and a conversational AI assistant (02:09) that can provide endless, personalized style advice.
UX highlights
- Clear Input Guidance: The visual guide for photo submission (00:25) is a brilliant way to prevent user error and manage expectations.
- Digestible Results: Presenting the color analysis in a swipeable, card-based format (01:41) breaks down dense information into manageable, thematic sections.
- Interactive Exploration: The app allows users to tap on individual colors within a palette (01:43) to see their name and hex code, adding a layer of interactivity.
- Conversational Follow-up: The AI chatbot (02:09) provides ongoing value by answering specific user questions, turning a one-time result into a continuous style dialogue.
- Contextual Prompts: The AI offers relevant, pre-written prompts like 'Help me pick an outfit' (02:11), reducing the friction of interacting with a chatbot.
- Theatrical Feedback: The 'Analyzing...' animation with a scanline (00:44) makes the AI's process feel tangible and sophisticated.
Monetization & growth
The video does not show a paywall or any clear monetization strategy. The app's primary focus seems to be on user acquisition and engagement. There is, however, an aggressive growth loop implemented very early in the user journey. At 00:10, before the user has even completed onboarding, the app presents a warm-up screen asking for a review, followed by the native iOS rating prompt. This tactic aims to capture initial user excitement.
Who it’s for
This app is for individuals interested in fashion, beauty, and personal styling who want to understand which colors complement their natural features. It's targeted at users who may have heard of seasonal color analysis but want a quick, accessible, and AI-driven way to get their results without booking a professional consultation. The AI assistant makes it particularly useful for those who want ongoing, practical advice on building a wardrobe and choosing makeup.
Notes & opportunities
The onboarding flow contains significant friction before delivering value. The user is hit with a rating request (00:10), a notification prompt (00:20), and a photo access prompt (00:32) all before seeing the core product. Delaying the rating and notification prompts until after the user has received their valuable color analysis could improve completion rates and lead to more informed, positive reviews. The results are great, but the path to get there could be smoother.