Gallang - High Quality Moodboarding Assets
Problem
Designers often create mood-boards, for example using Pinterest or Miro, to explore the design space. The images provided by these resources are often inconsistent in quality and the recommendation engines hinder, to some degree, truly imaginative and free inspiration. A digital mood-board bringing together several different sources of high-quality media, for example, stock photography, art galleries, fonts, etc. could lead to more effective exploration of the design space.
Research
In order to understand the target user and their needs, the project team conducted desk research, competitive analysis of mood-boarding tools as well as unstructured interviews with graphic and user interface designers. The results were collected and discussed to focus on a specific user problem to address as well as to inform the general information architecture of the resulting application.
Ideation and Development
Based on the results of the user and design research, individual and collaborative ideation and development techniques were used to create concepts, sketches, wireframes, mockups and finally an interactive Figma prototype. This prototype was tested with target users to evaluate the overall user experience as well as to identify potential usability issues before beginning the translation of the graphical prototype into code.
Delivery and Testing
The resulting web application was developed using JavaScript, more specifically implementing a Model-View-Presenter architecture using the React.js framework. The development was executed using remote pair programming and distributed agile methods. The final result is publicly available on Heroku at https://gallang.herokuapp.com.