Speed is the defining characteristic of modern software development. While engineering teams deploy code updates daily or even hourly through continuous integration and continuous deployment (CI/CD) pipelines, localization processes often remain stuck in a slower, waterfall-based cadence. This disconnect forces companies to make a difficult choice: delay a global release to wait for translations, or ship the English version immediately and alienate international users.
Continuous localization resolves this conflict by integrating translation directly into the software delivery lifecycle. By leveraging advanced machine translation (MT) applications and API-driven workflows, enterprises can automate the extraction, translation, and re-integration of strings. This approach ensures that multilingual content is ready simultaneously with new features, transforming localization from a bottleneck into a seamless, automated process.
The shift to continuous localization
Traditional localization models operate in batches. Developers finish a feature, export a resource file, email it to a localization manager, and wait days or weeks for the translated file to return. In an agile environment where sprints last two weeks, this latency is unacceptable. Continuous localization fundamentally changes this dynamic by treating language assets as part of the codebase, updated incrementally alongside the software itself.
Why waterfall workflows fail in agile environments
The “freeze and handoff” method of waterfall localization creates significant friction in agile teams. When developers must pause work to manually extract strings or wait for translations before closing a pull request, velocity suffers. This manual handling of resource files – such as JSON, XML, or PO files – often leads to version control conflicts. If a developer modifies the source code while the translation file is out for localization, merging the returned file becomes a complex, error-prone task.
In addition, batch processing often leads to severe context issues. Translators receive a large file without seeing the user interface (UI), leading to errors that are only caught after the translated build is compiled. This retroactive bug-fixing cycle increases the total cost of ownership and delays time-to-market. By the time linguistic bugs are identified, the development team has often moved on to the next sprint, making remediation costly and disruptive.
The role of AI in synchronizing code and content
Artificial intelligence, specifically Large Language Model (LLM) based machine translation, is the engine that makes continuous localization viable at scale. Unlike older statistical models, modern solutions like Lara are optimized to preserve placeholders, variables, and markup structures commonly found in software strings, reducing formatting errors that generic MT engines may introduce. Generic LLMs often struggle with the nuances of software strings, where a single word like “Save” could mean preserving a file or rescuing a character in a game.
Lara is designed to understand the broader context of the request, preserving placeholders, variables, and formatting tags while delivering fluent translations instantly. This capability allows teams to run a “pseudo-localization” or a high-quality MT pass immediately after a string is committed. By verifying UI expansion and layout compatibility before a human linguist ever touches the text, developers can catch internationalization (i18n) bugs early in the pipeline, ensuring a cleaner build for final review.
Integrating MT into CI/CD pipelines
To achieve true continuity, translation must be triggered automatically by events within the development platform. This requires a robust orchestration layer that sits between the code repository and the translation engines.
API-driven translation workflows
A sophisticated API is the backbone of continuous localization. Platforms like TranslationOS provide the necessary infrastructure to manage these interactions programmatically. When a developer commits new strings to a repository, a webhook triggers a call to the TranslationOS API, sending the new content for immediate processing. This eliminates manual file handling and ensures that the localization process begins the moment code is written.
The API serves as an intelligent router. It can analyze the incoming request based on metadata such as file path or branch name. Critical UI strings might be routed to a premium workflow involving Lara followed by human review, while lower-priority documentation updates might be processed by AI with a light post-edit.
Connecting repositories for automated handoffs
Direct integration with version control systems such as GitHub, GitLab, or Bitbucket is essential for reducing friction. Through dedicated connectors, TranslationOS monitors specific branches for changes in resource files. When a change is detected, the platform automatically creates a translation job.
Once the translation is complete, the system opens a pull request with the localized files, allowing developers to merge the translations just as they would a code patch. This “code-in, code-out” workflow keeps developers focused on building features rather than managing spreadsheets. It aligns localization with the developer’s native environment, treating translation errors as bugs and translation updates as standard commits.
Automating content updates with AI
Automation extends beyond simple file transfer; it encompasses the intelligent generation of the translation itself. In a continuous workflow, the quality of the raw machine translation output is critical because it determines the speed of the subsequent human review.
Leveraging Lara for context-aware drafts
One of the biggest challenges in software localization is the lack of context. A string like “Home” could refer to a navigation button or a dwelling. Lara, Translated’s proprietary LLM-based model, addresses this by analyzing the full document context or surrounding code comments.
By understanding the broader scope of the application, Lara delivers accurate initial drafts that respect terminology and style guides. For example, if a glossary defines “Account” as a specific technical term, Lara adheres to that definition across all languages. This high-quality baseline significantly reduces the Time to Edit (TTE) required for human reviewers, accelerating the entire pipeline. TTE is a critical metric here; it measures the average time a professional translator needs to edit a machine-translated segment. Lower TTE values correlate directly with higher AI accuracy and faster turnaround times.
Reducing latency with adaptive models
Static MT engines often repeat the same errors until they are manually retrained, which is a slow and periodic process. In a continuous workflow, this repetition is inefficient and frustrating for reviewers. Adaptive MT systems update the model’s dynamic memory in real time, influencing subsequent suggestions without retraining the full model.
As professional translators finalize strings in the TranslationOS loop, the underlying model updates immediately. This means that if a specific term is corrected in the morning, the AI will use the correct term for a similar string committed in the afternoon. This creates a cycle of continuous improvement, where the AI becomes increasingly attuned to the company’s specific voice and terminology, steadily reducing the manual effort required for each subsequent release.
Managing quality in continuous workflows
Speed should never come at the expense of accuracy, especially for user-facing software where clarity is essential. The most effective continuous localization strategies employ a “human-in-the-loop” model, where AI handles the heavy lifting and professional linguists provide the final polish.
Implementing human-in-the-loop validation
While Lara generates high-quality initial translations, cultural nuance and specific brand voice often require human judgment. TranslationOS facilitates this symbiosis by automatically assigning the best-suited professional to review the AI’s output. This assignment is powered by T-Rank, an AI system that analyzes the content’s domain and matches it with a translator who has proven expertise in that specific subject matter.
In a CI/CD context, this happens in parallel with other development testing. Just as code undergoes unit testing and peer review, content undergoes AI translation and human validation. Because T-Rank selects linguists who are familiar with the subject matter, the review process is rapid, ensuring that the localized strings are ready to be merged by the time the code is ready for deployment.
Monitoring quality with EPT and TTE
Data-driven quality management is essential for optimizing continuous workflows. Teams should track Errors Per Thousand (EPT) to benchmark the linguistic accuracy of the final output. EPT provides a standardized way to measure quality across different languages and projects, ensuring that speed does not degrade the user experience.
Simultaneously, monitoring Time to Edit (TTE) provides insight into the raw performance of the AI model. A decreasing TTE indicates that the adaptive model is learning and becoming more efficient, validating the return on investment (ROI) of the automated pipeline. By presenting these metrics in real-time dashboards, TranslationOS gives localization managers the visibility they need to optimize workflows and demonstrate the value of continuous localization to stakeholders.
Use cases: Software
The application of machine translation in continuous localization is transforming how software is built for a global audience. It moves localization from a post-development afterthought to a core component of the product strategy.
Accelerating SaaS product releases
For Software-as-a-Service (SaaS) platforms, new features are a primary driver of retention. A continuous workflow allows these companies to release a new dashboard widget or settings option in 20 languages simultaneously. By using TranslationOS to automate the handoff, SaaS providers ensure that their global user base receives value at the exact same moment as their English-speaking users. This prevents feature fragmentation, where international users are perpetually one or two versions behind, and helps maintain a unified global brand experience.
Scaling mobile app localization
High-growth mobile apps often face the challenge of updating app store descriptions and in-app content across dozens of markets weekly. Glovo, a leading multi-category app, leveraged this approach to manage hyper-growth. By integrating continuous localization workflows, they were able to launch in new markets rapidly while maintaining a consistent voice. The combination of AI speed and human validation allowed them to scale their operations in different markets. This ability to decouple growth from headcount is a key advantage of the AI-powered continuous localization model.
Conclusion
The integration of machine translation into CI/CD pipelines represents a fundamental shift in how software is built for a global audience. By moving from waterfall handoffs to continuous, API-driven workflows, companies can eliminate the trade-off between speed and reach. With platforms like TranslationOS orchestrating the process and advanced models like Lara delivering context-aware drafts, developers can deploy code with confidence, knowing that their message will resonate in every language. Contact us to learn more!