Role
Product Designer
Team
1 UX Researcher - 1 PM - 1 Product Designer (Me)
Timeline
3 Months
Platform
Web App
Link
Status
Live
01 — Overview
An AI powered design platform that transforms ideas into high-fidelity UI designs, wireframes, sitemaps and production ready code within seconds. Built for UI/UX designers, agile teams, startups and agencies, it streamlines workflows by converting simple text prompts or paper sketches into polished digital products.
This project is protected by a Non-Disclosure Agreement (NDA), so I can only share the full case study upon request. However, the images below highlight some of the polished visuals featured in the live product.
02 — The Problem
Product, non-technical and design teams often spend a significant amount of time creating MVP designs and turning ideas into usable interfaces. They frequently depend on designers to bring these ideas to life, making it challenging to communicate and translate ideas quickly and accurately between teams.
For design agencies, the challenge is even greater. Repetitive tasks such as blogs, Terms & Conditions pages and other common screens are recreated across multiple projects, taking up valuable design and development time.
We wanted to bridge the gap between an idea and a working product helping teams turn their ideas into live experiences faster, while reducing their dependency on designers and developers for repetitive tasks.
03 — My Role
As Product Designer on UXMagic, I shaped how designers and teams interact with AI-generated wireframes and UI, balancing speed with design quality.
01. Product Strategy
Defined how AI-generated output should fit into real design workflows.
02. Interface Design
Designed the prompt-to-design flow, from input to editable output.
03. Design System
Built a flexible component library to support rapidly evolving AI features.
04. User Testing
Tested with designers and agencies to refine trust in AI-generated results.
04 — Research
We started with a discovery workshop to understand the business and user pain points, identify key business goals and define the problems we needed to solve.
From there, we conducted desk research to understand the business, market, competitors and real user needs. We also used a combination of qualitative and quantitative research to validate assumptions and uncover deeper user pain points.
Based on these insights, we created prototypes and conducted A/B testing to evaluate different solutions and identify what worked best. We continuously used feedback, research findings and test results to refine the product and improve the overall user experience throughout the design process.
05 — Information Architecture
We organized UXMagic around the core user journey discover, create, refine and share. The dashboard acts as the central hub, giving users quick access to their projects, AI-powered creation and templates, while secondary areas such as tutorials and pricing remain easy to find without distracting from the primary design workflow.
This structure helped create a clear hierarchy, reduce cognitive load and make the AI creation experience the primary focus.
06 — Design System
UXMagic’s design system is intentionally minimal, keeping AI-generated content at the center of the experience.
We followed a 4-point grid system, using multiples of 4px and 8px for spacing, padding, margins and component dimensions. This helped us maintain a consistent, clean and scalable interface while keeping the visual language simple and minimal.
The system was designed with future expansion in mind, making it easier to introduce new components and features without compromising consistency across the product.
Typography
Colors
Components
Spacing Scale
07 — Final Output
The final product turns simple prompts into production-ready wireframes and UI, editable directly inside the platform.
08 — Impact
Since launch, UXMagic has helped teams move from idea to editable UI in minutes rather than days.
Conversion Rate
0% → 17%
Engagement
0% → 32%
Screen dsgn.
50+
Live
Active dev.,
09 — Reflection
Designing for an AI-powered tool meant designing trust into every generated result, not just the interface around it.
Trust is the real feature
Users needed to trust AI output before they'd rely on it.
Editability matters most
Generated results only mattered if they were easy to refine.
Constraints guide AI, too
Design constraints improved the quality of generated output.
Speed changes behavior
Faster iteration changed how teams actually used the tool.