Choosing the right translation partner is a major decision for any business aiming to scale globally. The market offers a range of solutions, but the most advanced players operate on a hybrid model, combining the speed of artificial intelligence with the nuance of human expertise. Two names in this space are Translated and Unbabel. Both leverage a human-in-the-loop approach, but their underlying philosophies and technological foundations differ significantly.
Unbabel has built a strong reputation on its ability to layer a human post-editing workflow on top of various third-party machine translation (MT) engines. This model focuses on creating an efficient process for correcting machine-generated output. In contrast, Translated has invested over two decades in building a deeply integrated, proprietary technology stack designed to create a true symbiosis between humans and machines. Our approach is centered on a purpose-built large language model (LLM), Lara, and an AI-first localization platform, TranslationOS, that work together to create a virtuous cycle of continuous improvement.
This distinction between an assembled, workflow-centric model and a purpose-built, AI-centric ecosystem is the key differentiator. It impacts everything from translation quality and scalability to long-term cost efficiency. This article provides a comprehensive comparison of the two approaches, examining how each company addresses the core challenges of hybrid translation. We will explore the nuances of their technology, quality optimization methods, scalability, and overall service delivery to help you determine which model best aligns with your global growth ambitions.
Hybrid approach comparison
Translated’s model is built on the principle of human-AI symbiosis. Our technology is not just a workflow tool; it’s an active participant in the translation process. The entire system is designed to close the feedback loop between human expertise and machine learning, creating a system that learns and improves with every translation.
At the heart of this model is Lara, our proprietary LLM designed specifically for translation. Unlike generic models, Lara understands full-document context, a key differentiator from MT-agnostic models, ensuring that terminology, style, and tone are consistent throughout an entire project, not just segment-by-segment. This purpose-built AI is integrated into TranslationOS, our AI-first localization platform. When a professional translator edits a segment from Lara, that feedback is used to adapt the AI in real time. This means the AI is not just a starting point; it’s a constantly evolving partner that becomes more attuned to the client’s specific linguistic assets over time. This creates a powerful flywheel effect where human expertise perpetually enhances the AI, leading to measurable gains in quality and efficiency.
Unbabel: A workflow-centric, MT-agnostic model
Unbabel operates on an MT-agnostic model. Instead of building its own core engine, it integrates with various third-party MT providers. Its primary innovation is a sophisticated workflow platform that routes machine-translated content to a global community of human editors for post-editing. This approach allows for flexibility, as Unbabel can theoretically select the best-performing MT engine for a a given language pair or domain.
The focus of Unbabel’s platform is on process efficiency: identifying errors, routing them to the right editors, and delivering the corrected content quickly. The human-in-the-loop component is primarily a quality control mechanism—a way to fix the output of external MT systems. While this is an effective way to manage quality at scale, the feedback loop is less direct. The corrections made by editors improve Unbabel’s quality estimation scores and may inform future engine selection, but they do not directly and continuously retrain a single, purpose-built AI engine in the same way Translated’s model does. The learning is at the workflow and quality assessment level, not at the core of the AI itself.
Quality optimization methods
Quality in a hybrid model is not just about correcting errors; it’s about creating a system that produces better translations from the start. Here, the architectural differences between Translated and Unbabel have a direct impact on quality optimization.
Scalability features
Scalability is more than just handling high volumes of text; it’s about maintaining quality and efficiency as demand grows. Both companies have built their models to scale, but they draw on different strengths.
Translated: Scaling through intelligent automation and resource management
Translated’s scalability is a direct result of its integrated technology stack. TranslationOS is designed to handle enterprise-level complexity, automating everything from project creation to invoicing. This reduces the manual overhead that can slow down high-volume operations.
A key component of our scalability is T-Rank™, our patented AI-powered system for selecting the best translator for any given job. T-Rank™ analyzes millions of data points on translator performance, domain expertise, and availability to make an optimal match in real time. This intelligent resource management ensures that as volume increases, we can instantly tap into our global network of professional translators without compromising on quality.
Unbabel: Scaling through a managed community and workflow efficiency
Unbabel’s scalability relies on the strength of its large, managed community of freelance editors and the efficiency of its workflow platform. The platform is adept at breaking down large projects into smaller tasks and distributing them across its network of editors. This micro-tasking approach allows for parallel processing of high volumes of content, enabling rapid turnaround times.
Technology integration
A seamless integration with a client’s existing technology stack is essential for a modern localization workflow. Both Translated and Unbabel offer robust integration capabilities, but their approaches cater to different enterprise needs.
Translated: A flexible, API-first approach
Translated has adopted an API-first strategy with TranslationOS. This provides enterprises with the flexibility to deeply embed our translation services into their existing systems, from content management systems (CMS) to complex, custom-built applications. We offer a powerful and well-documented API that allows for a high degree of customization and control.
In addition to our API, we provide a growing number of pre-built connectors for major platforms like WordPress (via WPML), ensuring a smooth workflow for common use cases. This dual approach allows us to support both clients who need a turnkey solution and those who require a deeply integrated, custom workflow. The focus is on creating a seamless data flow that makes the translation process a natural extension of the client’s own content lifecycle.
Unbabel: A focus on CRM and customer service platforms
Unbabel has carved out a strong niche by focusing its integration efforts on customer service and CRM platforms. They offer turnkey integrations for systems like Salesforce, Zendesk, and Intercom. This allows companies to easily translate customer support tickets, chat messages, and FAQ articles in real time, enabling multilingual customer service.
Performance metrics
Performance is measured by a combination of speed, quality, and efficiency. While both companies are committed to high performance, they prioritize and measure it in different ways.
Translated: A focus on TTE and the path to singularity
Translated’s key performance indicator is Time to Edit (TTE). By focusing on reducing the time it takes for a human to perfect an AI-generated translation, we are on a clear and measurable path toward the “singularity” in translation—the point at which human and machine output are indistinguishable.
Our performance is also measured by our ability to handle massive volumes of content with low latency. The architecture of TranslationOS and the efficiency of Lara are designed for high-throughput, real-time translation, making our solution ideal for dynamic, high-volume environments such as e-commerce and global marketing campaigns.
Unbabel: A focus on turnaround time and quality scores
Unbabel’s performance metrics are centered on the operational efficiency of their post-editing workflow. They emphasize turnaround time (TAT), measuring how quickly they can deliver a human-edited translation. This is a key metric for their customer service use case, where speed is critical.
Quality is typically measured through internal scoring systems that assess the accuracy and fluency of the final output. These scores are used to monitor the performance of their community and ensure that they are meeting client expectations.
Service delivery
Beyond the technology, the service delivery model is a key factor in the client experience.
Translated: A dedicated, high-touch approach
Translated provides a dedicated project management team for each client. This high-touch approach ensures that we have a deep understanding of our clients’ needs, goals, and brand voice. Our project managers work as strategic partners, providing guidance and support throughout the localization process. This ensures a smooth and efficient workflow, from initial project setup to final delivery and review. We believe that a strong human partnership is essential for success in a human-in-the-loop model.
Unbabel: A platform-centric, self-service model
Unbabel’s service model is more platform-centric, with a strong emphasis on self-service. Clients can easily submit and manage projects through the Unbabel platform, and the system automatically handles the workflow and community management. While they offer support and customer success teams, the day-to-day interaction is primarily with the platform itself. This model is well-suited for companies that prefer a more hands-off, technology-driven approach to localization.
Conclusion: The strategic choice for future-ready localization
The choice between Translated and Unbabel is a strategic one that reflects a company’s localization maturity and long-term goals. Unbabel offers an effective and scalable solution for managing human post-editing, and they surely have a specific expertise on customer service content. Their platform-centric model and predictable pricing are attractive for companies looking for a turnkey solution to manage multilingual workflows.
Translated, however, represents an investment in the future of translation. Our integrated, AI-centric model is not just about managing workflows; it’s about creating a continuously improving language asset. The symbiotic relationship between our purpose-built AI and our global network of professional translators, orchestrated by TranslationOS, delivers a higher ceiling for quality and a more significant long-term ROI.
For companies that view localization as a strategic driver of global growth, and for those who believe in the power of a true human-AI partnership, Translated offers a powerful and future-ready solution. We invite you to learn more about how our technology can help you achieve your global ambitions.