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Translation Generative AI Technology

Safety Text Translation at Scale for AI Content Moderation

The Challenge

A global technology company was developing AI-powered content moderation tools designed to detect abusive and harmful online content. To support this initiative, the client needed to localize and post-edit large volumes of safety-related text including messages, emails, and notifications across multiple languages.

Although the content had already been machine-translated, human post-editing was required to ensure contextual accuracy, cultural relevance, and appropriate local tone. The output would be used to train AI models, making linguistic precision and consistency critical.

Key challenges included:

  • Managing highly sensitive and potentially offensive content that required experienced linguists
  • Ensuring local adaptation—not just linguistic correctness—across diverse markets
  • Addressing technical complexity, as all content was delivered in CSV files that were not immediately ready for bilingual review

The client needed a partner capable of balancing scale, speed, quality, and technical execution in a safety-critical AI context.

• • • •The Solution• • • •

DataForce implemented a localization and MTPE workflow purpose-built for safety text and AI training data. A global team of 80 specialized linguists was rapidly mobilized to support 12 language pairs, spanning both European and Asian markets. Content was divided into structured batches and assigned based on linguist expertise, availability, and proven performance, helping maintain quality while meeting tight deadlines.

Quality management was embedded throughout the production process rather than treated as a separate step. Linguists received thorough onboarding and clear, client-specific guidelines focused on sensitive content handling, cultural nuance, and tone. These instructions were continuously refined between batches based on project manager feedback and prior learnings, enabling ongoing improvements in consistency and accuracy.

Project managers conducted regular quality checks with particular attention to local adaptation, such as:

  • Correct localization of personal names and cultural references
    (e.g., John → João, White House → Assembleia da República)
  • Tone appropriateness for safety and moderation use cases
  • Linguistic consistency across batches and contributors

To support efficient execution, DataForce’s technology team developed a custom technical workflow. Client-provided CSV files were pre-flighted and converted into bilingual review formats, allowing linguists to view source and target text side by side in PD. This approach reduced friction, minimized errors, and enabled linguists to work at speed without compromising accuracy.

Throughout the engagement, project managers, linguists, and the technical team worked in close coordination to ensure seamless delivery at scale.

Results

DataForce delivered high-quality, culturally accurate safety text localization at exceptional speed. Over the course of the project, 839,484 words were completed across multiple large batches.

Key outcomes included:

  • Delivery of a 218,684-word batch in just six business days, compared to a typical 45-day timeline
  • Consistent contextual accuracy and local relevance across all 12 language pairs
  • Reliable, high-quality training data to support the client’s AI content moderation models
  • Demonstrated ability to scale rapidly while maintaining control over quality and sensitive content

By combining linguistic expertise, adaptive quality management, and technical innovation, DataForce enabled the client to strengthen the reliability and effectiveness of its AI-powered content moderation tools in real-world, multilingual environments.