The challenge with traditional QA in translation
Traditional quality assurance in translation presents significant challenges for a company looking to scale effectively. These manual QA processes often lack the speed and precision required by modern businesses, especially those with time-sensitive needs. As the demand for rapid and reliable translation services grows, the limitations of traditional QA become more pronounced. Manual checks are not only time-consuming but also prone to human error, which can lead to inconsistencies and delays. This inefficiency is a significant pain point for users who require quick, simple, and reliable translation solutions.
Advanced methods: A proactive approach to quality
To meet the demands for speed and reliability, a truly modern quality assurance strategy must shift from being reactive to proactive. Instead of relying on downstream checks to catch errors after the fact, next-generation QA focuses on building quality into the translation process from the very beginning. This “upstream” philosophy is built on two core pillars: establishing a rock-solid data foundation and adopting metrics that measure true efficiency.
The data-centric foundation
At Translated, we believe that the highest quality output starts with the highest quality input. Our data-centric AI approach is built on curated, high-performance data that forms the backbone of our proprietary models, Lara. By focusing on the quality of data used to train our AI, we ensure that our translation outputs are not only accurate but also contextually relevant. This proactive approach to quality means that errors are prevented before they occur, rather than being caught after the fact.
This philosophy directly addresses the foundational principle of all machine learning: “garbage in, garbage out.” An AI model trained on messy, inaccurate, or out-of-context data will inevitably produce translations with the same flaws. A data-centric approach, by contrast, involves meticulous curation, cleaning, and annotation of training data.
Implementation strategies: Integrating advanced QA into workflows
A proactive quality philosophy is only as good as its implementation. To bring these advanced methods to life, they must be integrated directly into the localization workflow, creating a unified ecosystem where technology and human expertise collaborate. This involves using a central platform to manage the continuous improvement cycle and deploying intelligent systems to ensure the right human talent is involved at the most critical moments.
Continuous improvement with TranslationOS
TranslationOS is the cornerstone of Translated’s AI-first approach, serving as a comprehensive ecosystem that seamlessly integrates advanced QA methods into localization workflows. By leveraging AI-driven platforms like TranslationOS, enterprises can monitor and control translation quality at scale, ensuring that localized content meets the highest standards of accuracy and cultural appropriateness. This platform facilitates the automation of localization processes, enhancing efficiency and consistency in global content delivery.
Matching expertise with AI: The role of T-Rank™
Quality assurance in translation is not solely about technology; it also involves the human element. T-Rank™ exemplifies the power of human-AI symbiosis by revolutionizing how organizations find the perfect translator. This proprietary system intelligently assigns the most suitable human experts to each translation project, ensuring that the final review process is both faster and more effective. T-Rank™ uses advanced algorithms to identify the ideal linguist with the specific cultural acumen and domain knowledge required for a project.
Performance improvement: Measuring what matters
The value of a next-generation QA process is demonstrated through tangible gains in performance and efficiency. By shifting the focus to metrics that directly reflect business impact, organizations can move beyond purely academic quality scores. This allows for a clearer understanding of the return on investment from localization efforts and provides concrete data to prove the effectiveness of a proactive, technology-driven approach.
Beyond error scores: The business impact of TTE
When evaluating translation quality, traditional metrics like BLEU or MQM often fall short of capturing the true business impact. Time to Edit (TTE) emerges as a crucial indicator of success, providing a more accurate reflection of translation performance. A lower TTE means higher machine-translation quality, enabling faster time-to-market and greater reliability, all critical factors for time-sensitive users.
A look at the data: Quality gains in practice
The tangible benefits of an AI-first, proactive quality assurance model are best illustrated by real-world results. Consider the case of Asana, a leading work management platform that needed to scale its localization efforts to support a rapidly growing global user base.
Before: Asana’s localization process was heavily manual and could not keep pace with its continuous development cycle. This resulted in significant overhead, slow turnaround times for translations, and challenges in maintaining a consistent brand voice across dozens of languages. The process was a bottleneck to global growth, requiring extensive manual effort that was both costly and inefficient.
After: By partnering with Translated and implementing an AI-first localization strategy powered by TranslationOS, Asana transformed its entire workflow. The results were dramatic. Over 70% of the localization workflow was automated, which empowered Asana’s team by reducing manual effort by 30% and freeing them to focus on strategic initiatives instead of repetitive tasks. This newfound efficiency allowed them to accelerate their time-to-market for new features and content. Most importantly, this was achieved while improving the quality and consistency of their translations, ensuring their global brand voice remained strong and clear in every market. This is the power of next-generation QA in action: it turns localization from a cost center into a strategic driver of global growth.
Future development: The evolution of quality assurance
A commitment to quality is also a commitment to continuous innovation. Translation technology advances daily, and a forward-looking QA strategy must anticipate the next frontier. The goal is to evolve beyond preventing errors to predicting them, further streamlining the human-in-the-loop model and pushing the boundaries of what is possible in automated translation.
From reactive to predictive quality
The evolution of translation quality is shifting from a reactive to a predictive model, and this change is inevitable. Translated is at the forefront of this transformation, leveraging advanced AI to anticipate potential quality issues before they arise. By analyzing the source text and its context, our AI models can predict where errors might occur, allowing for preemptive adjustments.
Conclusion: Quality built upstream
Next-generation QA replaces slow, reactive checks with a proactive system built on curated data, predictive AI, and expert human review. By combining Lara’s context-aware translations, TranslationOS’s centralized oversight, and T-Rank™’s precise talent matching, organizations achieve faster turnarounds, higher consistency, and measurable gains reflected in metrics like TTE. To modernize your QA framework and scale quality with confidence, connect with Translated.