Yonnatech.

5 min readJul 18, 2023

Timeframe: 9 days

Team: 3 teammates

The first tool that uses AI to analyze and input data to generate multiple designs. Our challenge was to design a feature that allows users to edit and add prints o the design.

With Debora and Marta, I worked on our project for Ironhack UXUI course in collaboration with Yonnatech. They are an AI-based B2B software solution to shorten the design process.

After testing their B-version, we propose a feature and other solutions based on two weeks of our research and data.

The problem:

The Fashion design process is not creative but based on performance analysis and existing designs of a company combined with new trends, briefly edited, and launched.

Challenge:

A feature of this cloud-based software where people designing in the heavy fashion industry can generate designs, customize, create, and share collections with a few clicks. The design process can be streamlined and automatized and enhance productivity without waste.

How does it work?

Uploading data.
Generating design.
Select.
Save.

After researching for the first day of the project, we had an initial meeting with our stakeholders and kept the company’s strategies, goals, needs, competitors, and clients in mind. We began conducting qualitative and quantitative analyses.

As Yonnatech is a B2B SaaS (software as service CO), we had few suitable interviewees for our research. We specifically target experienced fashion designers for our Google Form questionary, selectively to this specific audience.

Surveys & Interviews

The creation process in the fashion industry could be more efficient in terms of cost and time.
Fashion designers still analyze trends, collect data, and manually create mood boards.
The design process has caused problems, including inefficient resource allocation, lack of focus on customers’ needs, overproduction, environmental issues, etc.

We developed three user personas to represent different segments of the target audience. They face challenges related to workload, visual expression of ideas, creating efficient prototypes, and need more tools for rapid and waste-free creations. The personas help us understand the target needs of people using the app according to our data while developing the product.

Define

We defined design problems and set objectives. We used How Might We (HMW) questions to simulate and brainstorm.

Moscow

We refined our feature.

Our quantitative research revealed that clothes are designed for all genders and sizes; designers use various communication and visualisation tools, and the design cycle for a piece can range from 1 day to 2 weeks.

Qualitative Analysis:

We conducted six in-depth interviews with designers from different backgrounds, including those in the heavy fashion industry and small and university students. We better understood their role, process, tools, and needs.

Affinity Diagram

Creating a visual framework was essential in synthesizing information from the interviews categorising the data provided a clearer. Understanding the problem enables us to analyse the insights effectively.

Benchmark Analysis

Tracked UX progress for product and its services.
Conducted comparisons with earlier versions of similar software we could access, competitors, benchmarks, or stakeholder goals.
Demonstrated the value of UX efforts through our research.

Job Stories:

Our tool is an app that provides a comprehensive way to modify, analyze, and share designs. Facilitates remote collaboration, employs AI for design generation and editing, and, as our data advise, must be suitable to heavy fashion, independent designers, and students.

User Flow

Create the project and upload the data.
AI generates a dress design based on preferences.
Select a design.
Make modifications.
Save, and share with the team.

The user flow outlines the streamlined dress project creation process with AI.

MidFi:

By analyzing existing tools and considering the default categories in the fashion industry, we tried to make it understandable with clear and familiar steps.

Usability Test

Valuable feedback from our interviewers led to improvements in our wireframes.

Added the option to select the prototype for editing
Included a 3D human avatar, considered a must-have feature.
Addressed missing steps in the loading process
Created a dedicated dashboard for working with 3d human avatars inspired by industry fashion 3d programs.

The enhancements align the tool with the expectations of the people using it.

Mood Board

We prioritize the design based on feedback from interviewees — the first choice is the most techie, modern, futuristic, and confident as our clients’ vision.