TrainAI powers tech leader’s AI data safety benchmarking
A leading technology company turned to TrainAI to benchmark AI data safety across vendors and nine languages. Mobilizing 285+ specialists and delivering up to 13,000 hours per month, TrainAI conducted multilingual audits that surfaced safety risks, vendor performance gaps, and new benchmarks for evaluating LLM training data. The result gave the client objective insights to guide vendor selection and strengthen global AI reliability.
Multilingual safety evaluation of AI data from 3 major vendors across 9 languages
Expert analysis from 285+ AI data and language specialists worldwide
Up to 13,000 work hours delivered per month
Detailed reporting on multilingual safety issues and AI data vendor performance gaps
Source of truth for strategic insights to support AI data vendor selection and risk mitigation
TrainAI by RWS mobilized 285+ specialists to deliver up to 13,000 hours per month, evaluating the safety of AI data from three major data vendors across nine languages, and providing detailed insights to guide the client in AI data vendor selection and risk mitigation.
A global technology leader in LLM development needed a way to objectively compare the safety of AI data across multiple data vendors and languages. With limited best practices for multilingual AI data safety, the company required a partner with deep AI data and linguistic expertise, scalable evaluation processes and vendor-agnostic objectivity.
TrainAI by RWS completed a comprehensive multilingual AI data safety audit that surfaced safety issues, flagged data vendor performance gaps and established new quality benchmarks for evaluating the safety of LLM training and fine-tuning data.
Redefining Multilingual AI Safety
As large language models (LLMs) continue to reach more diverse communities, the need for multilingual AI safety expertise is growing. For organizations working with external AI data vendors, quality assurance involves more than just tracking performance metrics alone. They also need to assess safety, bias and reliability across multiple languages, all while maintaining consistency in how outputs are assessed.
A leading global technology company discovered this firsthand when it set out to evaluate the safety and quality of AI data from multiple major vendors across different languages. To tackle the task, the company partnered with TrainAI by RWS and tapped into our comprehensive AI data services to audit the AI training data prepared by other vendors accurately and consistently.
Challenges
Ensure consistent AI safety evaluations across AI data vendors and languages
Build a cross-language quality framework to ensure fair, comparable results
Account for cultural nuance and language-specific AI behaviors at scale
Navigate the complexity of multilingual AI data safety assessments in a space with few established best practices
Deployed 285+ AI data and language specialists worldwide
Created a cross-language AI safety benchmarking and quality framework
Leveraged thousands of hours of AI data experience to develop proven workflows that met the audit’s specialized demands
Executed the multilingual audit with depth and precision and delivered comprehensive, language-specific AI data safety reporting
Ensured the wellbeing of AI data evaluators from the TrainAI Community through a complete wellness support and resilience program
Results
Conducted multilingual safety evaluations of AI data from 3 major vendors across 9 languages
Onboarded and delivered expert analysis from 285+ TrainAI data and language specialists worldwide
Delivered up to 13,000 work hours per month
Provided detailed reporting on multilingual AI data safety issues and vendor performance gaps
Became the client’s “source of truth” for strategic insights to support AI data vendor selection and risk mitigation
Background: assessing multiple vendors
A global technology company at the forefront of LLM development needed to conduct a comprehensive evaluation of AI safety data outputs from three major AI data vendors. This assessment spanned nine languages including French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Thai and Vietnamese.
To do it right, the company needed an objective, vendor-agnostic framework, one that could flag language-specific safety issues, ensuring consistency across evaluators and offering strategic insights to support AI vendor selection and risk mitigation efforts. They recognized the need for partners with both deep AI expertise and strong multilingual capabilities in this emerging field.
With a trusted two-year working relationship already in place, the company turned to TrainAI for support in multilingual AI safety evaluation.
3 major vendors across 9 languages
Up to 13,000 work hours delivered monthly
Maintained 96% agreement across languages
Challenges: consistency and multilingual complexity
At the start of the project, the global technology company faced several challenges that required sophisticated technical expertise and the ability to scale evaluations:
Ensuring consistent and objective assessments of AI safety outputs across multiple AI data vendors and languages
Developing a quality framework tailored for cross-language comparison and reporting
Navigating the complexity of multilingual AI safety assessments in a space with few established best practices
Accounting for cultural nuance, regional differences and language-specific AI behaviors at scale
Maintaining evaluation consistency proved especially complicated due to the subtle and often subjective nature of language. Safety assessments posed additional hurdles, such as differences of opinion among evaluators and the difficulty of maintaining consistency at scale.
Finally, the scope went beyond simple translation or localization. The audit required evaluators to understand cultural nuance, regional safety considerations and language-specific AI behaviors that could impact safety outcomes.
By combining systematic safety evaluation frameworks with global AI data and language expertise, TrainAI provided a leading technology company unprecedented visibility into multilingual AI data safety, helping them strengthen their LLM’s reliability globally.
Given the project's complexity and focus on safety, we used up to 285 in-house experts to conduct the evaluations. This approach helped us maintain quality standards while ensuring team members had appropriate support while analyzing AI outputs. Since our team was evaluating safety, that support incorporated a wellness and resilience program, complete with a licensed psychologist on hand to assist evaluators with their mental health needs.
We also developed a cross-language comparison framework that enabled standardized evaluations while surfacing language-specific issues. Our team’s deep expertise in multi-language AI evaluation was critical in this process.
AI data vendor information was anonymized to ensure equal treatment throughout the evaluation process, so that our audit team could remain focused on objective, language-based quality metrics, without introducing any unintentional vendor bias. This method strengthened the integrity of our analysis and provided the client with clear, impartial insights.
Creating a quality framework
Leveraging our global community of over 250k domain and language experts, we developed a systematic and scalable approach to multilingual AI safety benchmarking for the client.
At the core was a robust quality framework designed specifically for cross-language AI safety comparison. It established standardized evaluation criteria while accounting for linguistic and cultural nuances that could affect safety outcomes. Our team of GenAI data annotators ensured consistent assessments across both languages and AI data vendors.
Leveraging thousands of hours of experience
We approached the project with insights gained from previous large-scale AI engagements, including supporting major LLM deployments that involved 10,000 to 13,000 work hours per month. We adapted those proven workflows to meet the specialized demands of this audit.
Our focus remained on the linguistic contexts. By comparing AI data across different languages, we consistently identified language-specific issues such as spelling, grammar and fluency challenges that could impact AI safety and LLM performance.
Delivering comprehensive safety reporting
Our approach included detailed reporting mechanisms that highlighted language-specific safety issues while providing comprehensive vendor comparisons.
With our experience spanning 650 language pairs across 193 countries and millions of tasks delivered across many long-term AI data services projects, we had the scale and expertise to execute this multilingual audit with depth and precision.
Results: delivering clear insights across languages and vendors
The multilingual audit transformed the client's understanding of different AI data vendor capabilities across linguistic and cultural contexts.
Our evaluation revealed performance variations across data vendors and languages. We delivered detailed reports that highlighted each vendor's specific strengths and weaknesses in multilingual AI safety.
The audit also uncovered several critical insights, including:
Language-specific safety concerns, including variations in grammar handling, cultural appropriateness and safety alignment
Vendor performance gaps that varied significantly by target language and domain
Risk mitigation strategies tailored to specific language deployments and cultural considerations
Quality benchmarks that set new standards for multilingual AI safety evaluation
Despite the project's complexity, the collaboration was highly successful. TrainAI delivered unprecedented visibility into AI data vendor safety performance, established benchmarks for future evaluations and provided a clear path forward for improving safety and LLM outcomes.
Our team also delivered a high level of consistency, accuracy and compliance. Our quality goal for the program was 90-95% agreement. We exceeded that target, maintaining 96% agreement across languages throughout the entire lifecycle of the program.
Ensuring global AI safety through expert evaluation
This multilingual audit demonstrates how specialized AI safety evaluation can deliver critical insights for global technology deployment. By combining systematic evaluation frameworks with deep multilingual expertise, TrainAI enabled a major technology company to make informed AI data vendor decisions and navigate the complex safety landscape across diverse linguistic contexts.
Have a similar challenge? If you're grappling with multilingual AI safety, bias detection or vendor benchmarking, TrainAI can help. Our team brings the tools, expertise and scale to tackle complex evaluation projects across languages and domains. Let’s talk.
