The client set out to simplify a traditionally manual, fragmented process in the fashion industry - helping independent designers move from idea to production more efficiently. The product vision combined generative AI with a conversational interface to support users through this complex workflow. The challenge was to design a seamless, trustworthy, and intuitive experience that made a technical process feel approachable - particularly for users who weren’t familiar with AI or digital production tools. As the sole product designer, I needed to lead design from scratch while collaborating closely with a developer to bring the experience to life under tight time and resource constraints.
I began with research: speaking to fashion designers to map their workflows and pain points around production. Using these insights, I created user personas, journey maps, and wireframes that prioritised clarity, control, and trust. I designed a responsive web app in Figma and built the MVP using Framer, integrating with an AI backend to generate tech packs and deliver results via email. I focused on clean UI, minimal friction, and progressive disclosure to guide users from sketch to supplier match. Given the early-stage nature of the product, I prioritised testability and speed to feedback over polish.
I began by crafting a focused research plan to understand user behaviours, needs, and mental models. Insights from interviews and industry research helped identify key pain points - mainly around clarity, guidance, and trust in the process. I used these findings to shape a simple, guided journey that reduced overwhelm and introduced support at the right moments.
I mapped out the user flow and designed a conversational experience where users could input structured and unstructured information through a friendly interface. Knowing that trust is critical in AI-assisted tools, I paid close attention to microcopy, error states, and AI handoff moments, making sure the system felt supportive and transparent without overpromising.
I designed responsive screens in Figma, ensuring usability across devices, and used a modular layout that could scale with future features. I also collaborated with the developer in real time - sharing annotated prototypes, providing edge case guidance, and helping troubleshoot design-to-build gaps.
Throughout the project, I applied AI UX best practices such as progressive disclosure, user control, and clear feedback mechanisms - ensuring users could interact confidently with the system and easily correct or revise their inputs.
The client is set to launch a functional MVP that enables users to complete a complex, multi-step workflow with minimal friction. Early feedback has highlighted the ease of use, the clarity of the interface, and the helpfulness of the conversational flow. Users who previously relied on manual workarounds or guesswork found the tool intuitive and time-saving. Internally, the design system and documentation I created helped ensure smooth implementation and set a foundation for scalable future development.
Future improvements include refining the AI outputs based on user feedback, enhancing onboarding flows, and introducing ways for users to track and manage their submissions. Additional features focused on user education, system transparency, and dynamic input support are planned to deepen engagement and trust over time.
