Nexus Studio - AI Design System
In this project case study, I will be sharing my experience designing an intelligent design system platform that predicts component needs and optimizes tokens automatically. Throughout my internship working with design systems, I observed how much time designers spent searching for the right components and manually ensuring consistency across products. I saw an opportunity to use AI to predict what designers need before they ask and automatically maintain design system health. The goal was to create a next-generation design tool that combines predictive AI with enterprise-scale design system management, reducing design debt while accelerating team velocity.
Client
Portfolio Project
Type
Product Design
Year
2025
Process
Creating Predictive Component Intelligence From observing design workflows during my internship, I noticed designers repeatedly building similar components and struggling to discover existing options in large design systems. I designed an AI system that learns from these patterns across teams and proactively suggests components before designers know they need them. The interface presents these suggestions as elegant, floating cards with confidence indicators, making AI assistance feel collaborative rather than intrusive.
Building Intelligent Token Management During my work with design-to-code pipelines, I witnessed the manual effort required to maintain token consistency and accessibility compliance across multiple products. I saw how designers often unknowingly created accessibility violations or brand inconsistencies. Working with the concept of self-optimizing design systems, I created a token ecosystem that automatically adjusts for accessibility compliance while maintaining brand harmony. Colors cascade through semantic relationships, and the system provides gentle notifications when improvements are made behind the scenes.
Designing Cross-Platform Design System Governance I developed workflows that handle the complexity of enterprise design systems—multi-brand theming, component approval processes, and real-time synchronization between Figma and development environments. The interface maintains simplicity while managing sophisticated technical requirements.
Outcome
The final design includes predictive component suggestions, intelligent token optimization, and enterprise governance workflows that scale design systems across multiple product teams.
This concept demonstrates how AI can enhance design system adoption by reducing friction and decision-making overhead, while maintaining the systematic thinking that enterprise teams require.
The project showcased my understanding of design system architecture and positioned AI as a collaborative partner in managing complex design decisions at scale.
