SUPA partnered with a US-based Generative AI company (the Client) focused on refining AI models to generate design assets for diverse sectors, including game design, architecture, e-commerce, and marketing.
The Client needed to curate & label a varied dataset of 200,000 vector images to train their model in a short period of time. Given that their model needed to be able to produce design assets in diverse art styles, the corpus had to incorporate a massive variety of imagery split across multiple categories e.g. watercolor art styles.
With that, the Client needed to fulfill these workflows in a very short time:
The Client initially engaged multiple vendors which proved challenging to manage and lacked output diversity. These also resulted in quality issues, wasting a lot of time in their initial approach.
To overcome this challenge, the Client switched to SUPA as their vendor for sourcing & labeling this dataset. GenAI is a very new industry which meant experimentation is often necessary. This often translated to workflows needing to be amended, reworked or even abandoned very quickly. SUPA was able to match the Client’s needs in this by scaling workforces up & down as needed via:
All 200k images were successfully sourced, labeled, and sketched within the stipulated three months. Rework was required for only 3.5% of the total images, highlighting SUPA’s commitment to excellence.
Our partnership with the Client stands as a testament to our flexibility, adaptability, and commitment to quality. The project's success underscores our capability to align with client needs and deliver on large-scale projects, even when faced with technical challenges.