Case Study: Scaling High-Quality Multilingual Data with IndexAI

Client: Scale AI

Domain: Multilingual Text & Voice Data

Region: East & Southeast Asia

Challenge: Meet growing demand for high-quality multilingual datasets in underrepresented Asian languages for fine-tuning and evaluation of frontier AI models.

Deliverables:

• Recruitment and management of 1,000+ native contributors across 7+ Asian languages

• Delivery of structured datasets across multiple modalities: translation, rewriting, reasoning, voice, and evaluation

• Scalable quality management system with built-in reviewer workflows and issue resolution

Impact: Delivered consistent, high-quality multilingual datasets in languages that are traditionally difficult to scale—enabling Scale AI to serve clients training large multilingual models and advance evaluation for diverse language capabilities.

Meeting the Growing Need for Multilingual AI Data

As AI labs race to build frontier models with global capabilities, the importance of high-quality multilingual data—especially in underrepresented languages—has never been greater. In 2023, Scale AI partnered with IndexAI to support large-scale multilingual dataset development across Asian languages, with a particular emphasis on high-precision contributors, scalable project execution, and tight quality control.

While many providers focus on European languages, IndexAI specializes in East and Southeast Asian linguistic coverage, enabling Scale to expand its training and evaluation pipelines into more diverse linguistic territory.

The Client: A Leading Data Infrastructure Provider for Frontier AI Labs

Scale AI supports the most advanced AI labs and enterprises in the world, offering tooling and human-in-the-loop infrastructure for training and evaluating LLMs and other foundation models. In this case, Scale sought a regional partner that could supply structured, high-quality linguistic data in Korean, Japanese, Vietnamese, Thai, Traditional Chinese, and other complex language environments.

The Challenge: Multilingual Data that Actually Scales

The project faced four key constraints:

1. Linguistic diversity with native-level accuracy
   Many Asian languages require nuanced cultural and grammatical understanding, particularly when dealing with open-ended tasks like rewriting, paraphrasing, or chain-of-thought reasoning.

2. Scalable contributor sourcing
   Scale needed rapid sourcing of qualified annotators at volume—often under tight timelines.

3. Quality consistency across modalities
   The project included both text-based and voice-based datasets. Each modality required unique QA procedures, evaluation rubrics, and contributor skill profiles.

4. Rapid feedback and iteration
   New project types were frequently introduced, requiring contributors to quickly adapt to evolving instructions and edge cases.

The Solution: IndexAI’s Contributor Infrastructure & Workflow Engine

IndexAI responded with a full-stack multilingual workforce operation across the region, supported by local teams in Korea, Japan, Taiwan, Vietnam, and Thailand. Key features of the delivery framework included:

1. Sourcing & Onboarding Native Contributors

• Created language-specific talent pipelines using vetted networks and outbound campaigns

• Pre-qualified contributors through task-specific onboarding tests

• Achieved high retention through clear task instructions and dedicated community support

2. Workflow Design & Task Routing

• Built localized task flows for over 10 project types including sentence rewriting, translation, emotion tagging, and voice recording

• Integrated feedback loops between annotators, QA reviewers, and project leads

3. Modular QA Systems

• Implemented project-specific guidelines for text accuracy, logical consistency, and voice clarity

• Deployed reviewer layers across all data flows, using both automated flagging and human evaluation

• Established continuous feedback to contributors to improve quality over time

4. Cross-Modality Delivery

• Delivered thousands of paired text-voice datapoints in Korean and Japanese

• Applied structured templates for reasoning and multi-step outputs, enabling clean integration into LLM pipelines

• Ensured audio consistency through speaker calibration and pronunciation standards

Examples of Delivered Task Types

• Korean-Japanese Parallel Rewriting: Multiple-choice and open-ended sentence transformations

• Multilingual Chain-of-Thought Reasoning: Step-by-step logic annotations in native languages

• Voice Data Collection: Script-based and conversational voice data from verified native speakers

• Evaluation Tasks: Ranking and rating of model outputs for fluency, relevance, and reasoning

Each dataset was accompanied by structured metadata, reviewer logs, and performance breakdowns.

Outcome: A Trusted Partner for Multilingual Scale

Through IndexAI’s infrastructure, Scale AI was able to:

• Expand multilingual coverage without sacrificing quality

• Maintain delivery timelines across multiple concurrent projects

• Minimize rework and manual corrections through reliable QA

• Improve confidence in non-English language benchmarks and LLM fine-tuning

What began as a pilot engagement has now expanded into an ongoing partnership—with IndexAI acting as Scale’s multilingual arm in Asia, capable of mobilizing teams for emerging task types in as little as 48 hours.

The Impact: Foundation for Inclusive, Global AI

With IndexAI’s support, Scale delivered high-quality multilingual datasets to clients building next-gen language models. The effort has helped:

• Improve model performance across diverse language families

• Ensure cultural and linguistic inclusivity in benchmark and production data

• Lower the cost and complexity of sourcing and QA for low-resource languages

By combining local expertise with enterprise-grade project execution, IndexAI enables multilingual AI development that is both scalable and precise.