Case Studies

Leading AI company sources 7,200 skilled taskers for multilingual emotional evaluation

George Denison

|October 16, 2025

A leading AI developer used Prolific to recruit over 7,200 skilled taskers for complex emotional AI evaluation across several languages. With Prolific’s advanced filtering and self-serve flexibility, they were able to collect nearly 100,000 high-quality submissions in just three months.

The challenge: Finding skilled evaluators for nuanced emotional AI work

A leading AI company was developing a multimodal model with sophisticated emotional capabilities. As the model evolved, they needed to ensure it could accurately interpret and translate emotional tone across different languages and cultures.

This wasn't a simple task. Evaluators had to assess vocal translations in Spanish, Japanese, and Amharic and judge how emotionally accurate the outputs were. Each submission required:

Cultural fluency
Linguistic expertise
The ability to identify subtle emotional nuances

The team faced three key challenges:

Language expertise wasn't enough. They needed taskers with both translation skills and emotional intelligence. Understanding the words was only the starting point. Participants had to grasp how emotional tone shifts across cultural contexts.

Quality couldn't be sacrificed for speed. The model's development timeline was ambitious. But rushing through evaluation with inconsistent data would undermine months of work. Every assessment had to meet rigorous standards.

Scaling required control. As the project grew, the team needed to manage quality checks without building infrastructure from scratch. They wanted flexibility to adjust tasks and review submissions in real-time, not hand off control to a third party.

The solution: Targeted sourcing with complete quality control

Prolific gave the team exactly what they needed: access to a pool of skilled evaluators who could handle the complexity of the work, and the tools to maintain full control over quality.

The right expertise, fast

Using Prolific's advanced filters and pre-screeners, the team quickly found fluent Spanish, Japanese, and Amharic speakers with the skills to handle translation and emotional evaluation. There was no extended onboarding period. Participants were pre-qualified and ready to start delivering high-quality work almost immediately.

Self-serve quality management

Prolific's platform put the team in control of its own quality standards. The team could shape tasks exactly how it needed them, review submissions as they came in, and make adjustments on the fly. This level of agility proved essential as the team fine-tuned its approach throughout the project.

The team didn't have to build their own systems or rely on external quality managers. They had full visibility into the data pipeline and could respond fast if they spotted any issues.

Scalable, easy-to-use tools

Prolific's intuitive interface made it easy to launch targeted studies for each language group and manage thousands of participants. This meant the team could focus on collecting the data they needed to improve their model.

The results: Nearly 100,000 submissions in three months

In just three months, the team collected close to 100,000 high-quality submissions from more than 7,200 participants across multiple languages. The steady flow of reliable data meant they could move through evaluation quickly and stay on schedule.

Key outcomes:

~100,000 high-quality submissions collected across Spanish, Japanese, and Amharic
7,200+ skilled participants recruited without extended onboarding
Full quality control retained throughout the project

Prolific's pool of skilled evaluators helped them avoid resourcing bottlenecks. They hit their quality goals without delay and kept their model development on track.

Why quality matters in emotional AI

As AI models get better at understanding and expressing emotion, the stakes for accurate evaluation grow higher. Models that misread emotional tone or fail to translate emotional nuance across cultures can undermine user trust and limit adoption in global markets.

This project shows that rigorous human evaluation doesn't have to slow down development. With the right infrastructure and access to skilled taskers, teams can maintain both speed and quality standards, even for the most complex evaluation work.