During my graduation internship at the design agency Mandelo, I focused on solving a known issue for filmmakers - the lengthy music selection process for their film projects. The goal was to enhance the efficiency and intuitiveness while choosing music by offering an innovative solution. Stemming from a human-centered design approach, I created a strategic plan to address this problem using the double-diamond design process.

After conducting user research, I discovered that finding music through manual tag selection and filtering of catalogues can be time-consuming and exhausting. This process can lead filmmakers to settle for mediocre music search results, impeding their creative vision. Those findings inspired me to create a concept for an AI-based solution that suggests suitable music based on a given prompt that describes the film.

The idea underwent multiple iterations through wireframes and interactive prototypes, validated with various users and stakeholders. The AI functionality was possible by using the OpenAI API to convert the prompt (film language) to a fitting music description (music language). Sending the generated music description to the artificial intelligence software Cyanite.ai enabled the suggestion of suitable music alongside analysis of song moods, genres, instruments, and more.

The AI music discovery functionality was demonstrated through a working technical prototype made in Webflow, while UI mock-ups showcased the envisioned final product. Together, these artifacts showcase a concept that would provide filmmakers with a more informed decision-making process, guiding them toward the perfect tunes for their films.