Tattoo AI Design: Inked app icon

AI tattoo ideas & design generation

Tattoo AI Design: Inked

Inked helps users generate and explore tattoo concepts across styles with a fast, mobile-first workflow.

LiveBuilt with company team

Impact

Role

Mobile + backend support

Status

Live

Focus

AI tattoo ideas & design generation

Platform

iOS / Android

What I did

  • Built and refined key mobile flows with production-grade UX and reliability.
  • Supported backend integration points where needed to scale generation/processing workflows.
  • Helped maintain stable releases with iteration-friendly structure and clean UI patterns.

Highlights

  • Mobile flow development for high-frequency generation use cases
  • Backend integration support when needed for scalable generation
  • Quality-first UI details and stable release behavior

Challenges

  • Supporting high-frequency creation loops (generate → refine → save) without UI fatigue.
  • Balancing quality controls (style, constraints) with a workflow that stays ‘one-tap simple’.
  • Preventing drop-offs by making credits/subscription moments feel natural and non-blocking.
  • Keeping the app stable while experimenting with new styles, prompts, and flows.

Architecture and delivery

  • State-driven UI for generation pipelines (idle → generating → result → refine) with clear transitions.
  • Reusable UI components for style selectors, prompt helpers, and results grid to keep consistency at speed.
  • Network abstraction for generation endpoints + consistent error surfaces (empty states, retry patterns).
  • Scalable assets pipeline for storing favorites/history while remaining backend-ready for expansion.

Tech stack

FlutterMobileBackend (as needed)

Problem

Users struggle to translate an idea into a clean tattoo concept and want quick variations across styles.

Solution

A guided generation flow with fast iteration and a mobile UX optimized for browsing, saving, and refining designs.

Outcome

Live on the App Store with a foundation designed for scalable content generation and future feature growth.