TransPerfect, a global leader in language and technology solutions, successfully implemented a production-grade LLM system to enhance their translation services. This case study demonstrates a practical application of LLMs in a production environment, specifically focusing on two crucial workflows: automatic post-editing of machine translations and AI-assisted transcreation of creative content. The company faced significant challenges in scaling their translation operations while maintaining quality and reducing costs. Their GlobalLink translation management system processes billions of words annually across multiple languages, making it a perfect candidate for AI automation. However, the implementation needed to address several critical requirements, including data security, quality control, and maintaining the nuanced understanding required for creative content translation. ## Technical Implementation and Architecture TransPerfect's solution leverages Amazon Bedrock as the core LLM infrastructure, integrating it with their existing GlobalLink Enterprise platform. The implementation includes several key components: * Translation Memory (TM) System: Acts as the first layer of translation, utilizing previously approved translations * Machine Translation Layer: Powered by Amazon Translate for initial translation * LLM-powered Automatic Post-Editing (APE): Implements Amazon Bedrock models to improve machine-translated content * Quality Control System: Includes guardrails and contextual grounding checks to prevent hallucinations and ensure accuracy The system's architecture is designed to handle both technical content translation and creative content transcreation, with different workflows optimized for each type of content. ## Security and Compliance Considerations Data security was a primary concern in the implementation. The solution leverages Amazon Bedrock's security features to ensure: * No data sharing with foundation model providers * No use of customer data for model improvements * Compliance with major standards including ISO, SOC, and FedRAMP * Comprehensive monitoring and logging capabilities * Implementation of guardrails for responsible AI use ## LLM Operations and Quality Control The system implements several LLMOps best practices: * Prompt Engineering: Carefully crafted prompts incorporate style guides, approved translation examples, and common error patterns * Quality Monitoring: Over 95% of LLM-suggested edits showed improved translation quality * Human-in-the-Loop Integration: Strategic use of human reviewers for quality assurance and handling edge cases * Automated Guardrails: Implementation of Amazon Bedrock Guardrails to prevent hallucinations and ensure factual accuracy ## Workflow Integration and Automation The solution implements two distinct but related workflows: 1. Automatic Post-Editing Workflow: * Machine-translated content is processed through Amazon Bedrock LLMs * Style guides and approved translation examples inform the post-editing process * Content either proceeds to lightweight human review or direct publication * Results in up to 50% cost savings for translations 2. AI-Assisted Transcreation Workflow: * Utilizes Anthropic's Claude or Amazon Nova Pro through Amazon Bedrock * Generates multiple candidate translations with variations * Human linguists select and refine the most suitable options * Achieves up to 60% productivity gains for linguists ## Performance and Results The implementation demonstrated significant improvements across several metrics: * Cost Reduction: Up to 40% savings in translation workflows * Time Efficiency: Up to 80% reduction in project turnaround times * Quality Improvement: Marked improvement in machine translation output quality * Scalability: Successfully handling billions of words across multiple languages * Productivity: 60% gain in linguist productivity for transcreation tasks ## Challenges and Solutions The implementation faced several challenges that required careful consideration: * Creative Content Translation: Traditional machine translation struggled with nuanced, creative content. This was addressed by developing specialized LLM prompts for transcreation. * Quality Assurance: The risk of hallucinations and accuracy issues was mitigated through implementation of guardrails and contextual grounding checks. * Workflow Integration: The system needed to seamlessly integrate with existing translation processes, which was achieved through careful architecture design and API integration. ## Future Developments and Scalability The success of this implementation has positioned TransPerfect to further expand their AI-powered translation services. The system's architecture allows for: * Integration of new language models as they become available * Expansion to additional content types and industries * Further automation of translation workflows * Enhanced customization for specific client needs This case study demonstrates the successful deployment of LLMs in a production environment, showing how careful attention to security, quality control, and workflow integration can result in significant improvements in efficiency and cost-effectiveness while maintaining high quality standards. The implementation serves as a model for other organizations looking to integrate LLMs into their production systems, particularly in applications requiring high accuracy and security.