I. Problem

What is MVLP and What is a Localization Product?

MLVP or minimum viable localization product is a term that describes the initial stage of a localization product. Apart from human workflow orchestration, MVLP contains no human input apart from data management and curation that is used to train the AI responsible for generating the MVLP. It is a workflow that consists of a raw MT output, followed by a trained AI post-editing process and corresponds to the LangOps manifesto to “Build Language-agnostic”.

Localization product is a concept that can be compared to a software product in many ways. It is helpful to think about it in terms of DevOps practices and how product is manipulated and iterated in sprints until it reaches its final form. Each version of localization product presents different added value that is defined by localization sprint objectives.

II. Solution

Why We Need a Minimum Viable Localization Product?

MVLP serves as the base working product of localization. Having been machine translated and AI post-edited, it is a full placeholder for content that, while not finalized, can be used in in gathering data on user behavior and traffic analysis.

This was also the case where a client of Native Localization agreed to create a workflow that introduced real-time data into their localization decision-making.

A client, who is a software development company in the Fintech sector maintains a product platform, as well as the knowledge base for their customers to be able to use their product to its maximum efficiency. After Native had performed product string localization, it made sense to follow up with the localization of the knowledge base. However, their localization budget was spent for that fiscal year and this portion of content was not included, which was quite substantial as supporting documentation tends to be.

It would not be efficient from a UX perspective to have disparity between content, so the solution required a LangOps based approach, which would dictate that we must “leverage all data and tech” in efforts to make smart localization decisions. An MVLP was created for five main topic articles into 16 languages using DeepL MT engine, accompanied by OpenAI powered AI engine, trained with approved translation memory data and terminology data from previous localization work. The AI engine was further prompted on style, untranslatables, product names, etc. In this case this data was enough to reduce the parity towards human output to minimum. Articles were published in an MVLP form for a month. This provided enough Google Analytics data allowing Native to then execute a Blackbird automation which gathered the Analytics data and then created a report of which articles in which languages generated over 10 000 impressions, over 5000 impressions and over 2000 impressions. Based on the report Native proposed a staggered localization effort with three priority levels, giving our client a chance to invest in localization where it mattered the most.

A surplus solution was later applied to marketing, where MVLP was used in marketing A/B testing with smaller audiences. The previously trained AI model (now supplemented with marketing related data such as ICPs, content pillars, messaging intent, etc.) was used to gauge what ideas resonate more than others in 16 global markets at once. The data was later used for marketing specialists to draw insights from and create campaigns that performed on average 20% better than their previous ones. In addition to that, the insights cost fraction of what localized A/B testing would cost a year before.

III. Experiences, Benefits, and Metrics

Learnings and Benefits

Applying comparatively new LangOps concepts to real time use cases often provide a lot of conclusions and data for further iteration. In this case we learned that in order for MVLP to be as close to human parity, the data used to train the AI matters a lot. Results may warry, but in order to create a sufficient level MVLP, data classification and baseline has to be created to ensure the MVLP is actually useable.

MVLP significantly brings down the cost of A/B testing because the workflow is rather simple. Reduced costs potentially allow designers to be a bit more positively careless in their ideas, inviting more creativity and freedom without worrying about the expenses. Wider A/B testing means better live results.

MVLP invites live data to localization workflows. Current digital landscape relies on applying data as quickly as possible to create impact. MVLP is the first iteration of a localization product that is adjusted and polished with each localization sprint in efforts for the software to become a truly relevant and resonating on the global scale.

I. Problem

Language Service providers and internal language departments often face the challenge that they receive content for translation which is technologically and linguistically unsuitable for translation. In order to improve the quality of the source language, it is important to reach out to upstream processes and win them over as sponsors for a global content delivery process. Also, as stated in the LangOps manifesto, the world is changing from one-way communication paradigm to a conversation or bidirectional flow of information. And of course corporate end customers are demanding AI-driven solutions. LSPs and internal language services will have to cater to these new needs in order to stay relevant.

The challenge has been that traditional TMS and CAT tools are built for experts and not for upstream, non-linguistic stakeholders. Therefore it has always proven difficult to onboard content creators, developers, engineers and the like onto a common platform.

II. Solution

Our LangOps solution combines all the functionality and data access points which corporate end users need in order to interact with “language”. This includes manual and automatic content and translation project creation, like you find in traditional localization portals, of course. But much more than this, it also provides terminology retrieval, management and verification options, machine translation solutions, taxonomies and structure data, systematic translator query management which helps pinpoint content issues, review or quality management features and more. These functionalities are completely customizable to keep the user interface simple and deliver optimal, tailored user experience. That way, onboarding enterprise-wide stakeholders is much easier and faster.

On the back-end, our portal integrates with the traditional TMSs and BMSs, but also authoring tools, content management platforms and proprietary or commercial corporate tools which can consume language data. We make sure all these platforms are kept up to date on the data. By integrating linguistic assets into corporate tools and platforms, we bring their functionality directly to the end users and thus increase the benefits and values customers get out of them.

III. Experiences, Benefits, and Metrics

We believe our platform is a major step towards a true LangOps platform. It gives corporate users exactly the tools and data they require, integrates with all the required upstream and downstream processes and hides the complexities of language technology from those who do not need to be exposed to it directly.

It has made corporate language management much easier to use and spreads the benefit of linguistic assets to a much larger audience in corporate environments. This in turn makes it much easier to obtain budgets and define upstream processes to improve content and communication throughout the entire organization and in all languages.

I. Problem

In today’s globalized world, businesses often encounter significant challenges when it comes to software development and localization. The traditional silos between these two critical processes can lead to inefficiencies, delays, and even errors in the final product. This divide between software development and localization teams has long been a stumbling block for organizations striving for a global reach.

II. Solution

With LangOps—an innovative approach that serves as a natural extension to DevOps, uniting the worlds of software development and localization seamlessly. LangOps empowers organizations to break down the silos between these traditionally often separate domains, fostering collaboration and accelerating the delivery of localized software products.

III. Experiences, Benefits, and Metrics

With LangOps we accomplishes this by integrating localization considerations into the software design and development pipeline from the very beginning. Here’s how it works:

Early Integration: With LangOps, localization isn’t an afterthought; it’s an integral part of the development or even the design process. Designers and developers work alongside localization experts to ensure that internationalization is considered early on. This prevents common localization issues.

Continuous Localization: LangOps encourages continuous integration and continuous localization. As new features and updates are developed, they are simultaneously localized. This ensures that localized versions are always up-to-date and reduces the lag time between development and localization. This can be achieved with our L10n Portal and Services which include nMT and AI to automate steps along the way, leading to a lean and agile end-to-end process.

Automated Workflows: Automation plays a key role in LangOps. Automated testing, quality assurance, and deployment pipelines streamline the localization process, reducing the potential for human error and saving valuable time.

I. Problem

The demand for rapid, precise, and context-aware translations in the language services industry is at an all-time high. Traditional machine translation systems often miss the subtleties of language, requiring extensive post-editing and failing to meet the specific needs of diverse projects and clients. This challenge necessitates a solution that can understand and replicate the nuances of human language, adapt to various styles and tones, and integrate seamlessly into existing translation workflows.

II. Solution

GPT Integration in translate5

translate5’s innovative approach integrates Generative Pre-trained Transformer (GPT) technology as a customizable machine translation engine. This solution enables project managers (PMs) to create bespoke language resources tailored to each project’s unique requirements, leveraging:

Visual Translation Feature

translate5 offers a “What You See Is What You Get” (WYSIWYG) interface, allowing translators to work with the text within the layout for various source file formats, including CMS, Office, InDesign, video subtitling, and Android/iOS apps. This feature ensures translations fit the visual and cultural context of the original document, addressing challenges such as text length and layout compatibility.

Custom Training for GPT

PMs can train GPT with system messages, example data, and terminology, utilizing linguistic resources stored in translate5. This process, similar to onboarding a new translator with a style guide, ensures the AI’s output aligns closely with project expectations.

Collaborative Development and the Open Source Advantage

The successful integration of GPT within translate5 is the result of collaboration between the translate5 team, led by MittagQI, and World Translation. This partnership has facilitated technical development and ensured the solution meets the high standards required by professional translation services. As a third-generation open-source project, translate5 is backed by MittagQI, driving innovation, development, support and maintenance.

III. Experiences, Benefits, and Metrics

Evaluation and Impact

Translating technical documentation for Leica Geosystems from English to German showcased GPT’s capabilities, with its output compared against DeepL. Independent evaluations by experienced translators highlighted GPT’s fluency, idiomatic precision, and alignment with the client’s desired style and tone. Feedback emphasized GPT’s superior handling of style and readability, though noting the need for improvement in translation precision.

This advancement enables PMs to quickly create MT language resources in translate5, customized for each client or project. This transformation requires PMs to possess a deep linguistic understanding, making them prompt engineers who tailor AI output to client expectations, enhancing both efficiency and quality.

Conclusion

The integration of GPT into translate5 marks a significant advancement in translation technology, offering a customizable, efficient, and accurate solution for language service providers. This case study exemplifies the potential of AI and human expertise to meet the translation industry’s evolving demands, setting new benchmarks for quality and innovation. As translate5 continues to explore GPT’s use for various applications, it builds its leadership in leveraging AI to enhance language services.

I. Problem

The demand for rapid, precise, and context-aware translations in the language services industry is at an all-time high. Traditional machine translation systems often miss the subtleties of language, requiring extensive post-editing and failing to meet the specific needs of diverse projects and clients. This challenge necessitates a solution that can understand and replicate the nuances of human language, adapt to various styles and tones, and integrate seamlessly into existing translation workflows.

II. Solution

GPT Integration in translate5

translate5’s innovative approach integrates Generative Pre-trained Transformer (GPT) technology as a customizable machine translation engine. This solution enables project managers (PMs) to create bespoke language resources tailored to each project’s unique requirements, leveraging:

Visual Translation Feature

translate5 offers a “What You See Is What You Get” (WYSIWYG) interface, allowing translators to work with the text within the layout for various source file formats, including CMS, Office, InDesign, video subtitling, and Android/iOS apps. This feature ensures translations fit the visual and cultural context of the original document, addressing challenges such as text length and layout compatibility.

Custom Training for GPT

PMs can train GPT with system messages, example data, and terminology, utilizing linguistic resources stored in translate5. This process, similar to onboarding a new translator with a style guide, ensures the AI’s output aligns closely with project expectations.

Collaborative Development and the Open Source Advantage

The successful integration of GPT within translate5 is the result of collaboration between the translate5 team, led by MittagQI, and World Translation. This partnership has facilitated technical development and ensured the solution meets the high standards required by professional translation services. As a third-generation open-source project, translate5 is backed by MittagQI, driving innovation, development, support and maintenance.

III. Experiences, Benefits, and Metrics

Evaluation and Impact

Translating technical documentation for Leica Geosystems from English to German showcased GPT’s capabilities, with its output compared against DeepL. Independent evaluations by experienced translators highlighted GPT’s fluency, idiomatic precision, and alignment with the client’s desired style and tone. Feedback emphasized GPT’s superior handling of style and readability, though noting the need for improvement in translation precision.

This advancement enables PMs to quickly create MT language resources in translate5, customized for each client or project. This transformation requires PMs to possess a deep linguistic understanding, making them prompt engineers who tailor AI output to client expectations, enhancing both efficiency and quality.

Conclusion

The integration of GPT into translate5 marks a significant advancement in translation technology, offering a customizable, efficient, and accurate solution for language service providers. This case study exemplifies the potential of AI and human expertise to meet the translation industry’s evolving demands, setting new benchmarks for quality and innovation. As translate5 continues to explore GPT’s use for various applications, it builds its leadership in leveraging AI to enhance language services.

I. Problem

Thousands of translation and audio-to-text submissions ran through the localization department with manual processes annually. This was maintained by a significant administrative investment. Also, other departments, such as Editorial and Marketing departments, needed increasing quantities of transcription and translation services.

II. Solution

After a thorough review of available technology, the non-profit decided to design its own component-based language factory, with a set of microservices, tied together with the Blackbird.io workflow orchestrator as its backbone with a recursive microservice architecture.

A folder portal (or “hot folder”) was created on Dropbox in which translation or audio-to-text submissions can be placed. A submission is automatically classified based on file type and file name and automatically assigned to the appropriate semi-automated workflows. The file names are created with a simple-to-use file name builder on a Google sheet and those file names are automatically interpreted through Regex (regular expressions) classifications within Blackbird.io.

As each file submission travels through the workflow, the steps are semi-automatically updated on Slack channels and Trello (Kanban style) through Blackbird.io. Trello has its own automations set up to remove and assign individuals to cards at various steps. These product-based cards are templated in Trello and the workflow automatically copies the correct template based off the filename of the original submission.

Aside from translation submissions, Blackbird also enabled a microservice to be built for audio-to-text by using Transkriptor and OpenAI’s API and its Whisper feature. Through prompting and classifications, this microservice can transcribe audio, add paragraphs, timestamps and speaker diarization.

Aside from a MT-only microservice, another TMS microservice classifies files for TMS into four different domains in order to populate four different Translation Memories and Glossaries.

Other microservices can convert files automatically to use the correct one for each tool and can also be used to convert output files to the desired final file formats.

Once a file submission has triggered a workflow, the workflow is setup to add useful information to the files, such as word counts and it archives file versions to an archive folder automatically.

III. Experiences, Benefits, and Metrics

Manual administrative tasks have reduced by around 40 hours a week.

Various digital tools have been, or are in the process of being, sunsetted and replaced by API enabled equivalents and people outside of the localization department can submit translations and audio-to-text requests with ease.

LangOps staff can easily analyse and adjust each step of the process. 3rd party components of the workflow can easily be switched out or adjusted. Language assets can be used, independent from TMS, for further LangOps workflows and model training.

I. Problem

MakesYouLocal, an e-commerce localisation agency , specialises in helping online businesses thrive in new markets through localised customer service, marketing, and translation solutions. While they were already delivering quality translations, MakesYouLocal recognised an opportunity to enhance their efficiency and scalability to better serve their expanding client base. The goal was to increase productivity, reduce costs, and maintain the high quality their clients expected without compromising on speed or accuracy.

II. Solution

To achieve these objectives, MakesYouLocal partnered with EasyTranslate to implement HumanAI, an advanced technology that merges artificial intelligence with human expertise. This solution was designed to optimise their translation process, making it faster, more cost-effective, and scalable, while preserving the essential human touch that ensures cultural and linguistic accuracy.

Key elements of the solution included:

III. Experiences, Benefits, and Metrics

The integration of EasyTranslate’s HumanAI technology into MakesYouLocal’s operations brought substantial improvements, delivering significant benefits in terms of efficiency, cost savings, and translation quality.

1. Increased Efficiency:

– Result: The time required to complete translation projects was dramatically reduced, with projects that previously took 20 hours now being completed in just 2 hours.

-Impact: This tenfold increase in productivity allowed MakesYouLocal to take on more projects, enhance their service offerings, and better meet client demands without adding strain to their resources.

2. Cost Savings:

– Result: Translation costs were reduced by 90%, significantly lowering operational expenses.

– Impact: These savings allowed MakesYouLocal to maintain competitive pricing while increasing profit margins, further strengthening their market position.

3. Maintained High-Quality Standards:

– Result: HumanAI achieved an exceptional accuracy rate of one mistake per 1,000 words, far surpassing traditional benchmarks.

– Impact: This high level of accuracy ensured that MakesYouLocal could continue to deliver consistent, high-quality translations that met their clients’ expectations for brand voice and cultural relevance.

4. Enhanced Scalability:

– Result: With the efficiency gains and reduced costs, MakesYouLocal was able to scale its operations more effectively.

– Impact: They expanded their capacity to handle more projects, allowing them to grow their client base and enhance their competitive edge in the e-commerce localisation market.

Conclusion

The implementation of EasyTranslate’s HumanAI technology was a strategic move that allowed MakesYouLocal to optimise its translation processes, resulting in enhanced efficiency, significant cost savings, and maintained high-quality standards. By leveraging the strengths of both AI and human expertise, MakesYouLocal was able to better serve its clients, expand its business, and solidify its position as a leader in e-commerce localisation.

I. Problem

A rapidly growing SaaS company faced a pressing challenge: scaling its multilingual content operations to support a user base across 25+ countries. Their existing localization workflow was fragmented, relying on manual updates, email exchanges, and disconnected tools. This inefficiency led to delayed product launches, inconsistent translations, and mounting frustration among both developers and translators. The company needed a scalable, streamlined approach to manage localization, ensuring a seamless experience for their global audience without sacrificing speed or quality.

II. Solution

The company partnered with Crowdin to implement a comprehensive Language Operations (LangOps) strategy. Using Crowdin’s centralized platform, they integrated localization into their CI/CD pipeline, allowing real-time synchronization between their development tools and translation processes. The team utilized Crowdin’s advanced features, including:

Automation: Automated file management and content updates eliminated manual errors.

Collaboration: Translators, developers, and content creators worked together on a single platform, with real-time feedback loops.

AI-Powered Tools: Crowdin’s AI-assisted translation tools boosted translator productivity and maintained consistency across languages.

Custom Workflows: Tailored workflows ensured translations were reviewed and published with minimal disruption to the development timeline.

By embedding LangOps directly into their processes, the company turned localization from a bottleneck into a competitive advantage.

III. Experiences, Benefits, and Metrics

The transition to Crowdin’s LangOps platform delivered immediate and measurable results:

Faster Time-to-Market: Localization time was reduced by 40%, enabling simultaneous global launches.

Improved Quality: Consistency in translations improved by 35%, thanks to shared glossaries and translation memory.

Increased Team Efficiency: Automation and real-time collaboration cut repetitive tasks by 50%, freeing up resources for creative work.

User Engagement: Customer satisfaction scores improved in non-English-speaking regions due to more culturally nuanced and error-free translations.

The team praised Crowdin’s intuitive interface and robust API integrations, which fit seamlessly into their existing workflows. One project manager noted, “Crowdin transformed localization from an afterthought to a core part of our product development process.”

By adopting a LangOps mindset with Crowdin, the company achieved operational excellence, providing an optimal user experience across languages and accelerating its global growth.

I. Problem

An organisation with over 2500 language professionals, team leads, and managers was responsible for curating and localising millions of data points every month, the content produced was to be used in global products and services serving millions of users on daily basis.

Written documentation was the source of truth for the whole organisation, which included curation guidelines, style guides, language guides and SOPs that everyone followed for their daily operations, but as the products expanded in scale and scope, it became apparent that the existing decentralised structure of documentation was constantly challenging consistency, recency and usability across the organisation.

Products were localised into more than 50 languages, each with their own unique requirements that needed to be considered at all stages of the product’s life cycle. Such requirements were hosted sporadically across documents with various levels of access and ownership, leading to the aforementioned issues of consistency and accuracy.

Due to the distributed nature of the workforce which came from over 70 different countries, English was chosen as the official language for all communication and documentation, so a set of standards and quality measures were needed to always ensure clarity of instructions and purpose, and accessibility for all levels of English proficiency.

The vast number of cultures in the project also meant that guidance and training on cultural sensitivities was also needed for a cohesive and harmonious work environment that encouraged respectful collaboration.

II. Solution

A central platform to host all documentation that adhered to strict privacy and security requirements and provided workers of all levels with the required up-to-date documentation for their products and locales.

The new platform design started by identifying the needs of the different teams, and their existing documentation structure, which provided great insight on possible structures that could be applied to the design of the new system.

The new platform needed to meet the following requirements:

• Multilayered access levels for the different users of the platform.


• A clear and easy to navigate structure that enables users to find the information they need with ease.


• Easy editing and formatting tool to enable documentation creators and users to create and consume appealing and informative documents.


• A robust infrastructure with near 100% uptime due to the highly distributed nature of the organisation.

Due to the products nature and the high level of language integration, it was crucial to accommodate the specific needs of each language team in the requirement design, and how it fits within the overall structure of the knowledge management system. Language guidance was not only for linguists, but for anyone creating the product to use as a reference of what may be needed to accommodate specific language needs.

The platform was built based on the specified criteria, and work was ongoing to sift through the massive volume of documentation (around 7000 unique documents pre-migration) to evaluate the quality of the existing material and avoid any of its shortcomings.

Redundant and obsolete knowledge was identified and isolated, general level instructions were consolidated into general-purpose documentation, style guides and templates were standardised to ensure consistency of source language, language styles and terminology were reviewed, updated and publicised for the whole organisation, training was provided to all stakeholders on the new platform and pilot programs were conducted to ensure stability and proper functionality before the full scale migration.

III. Experiences, Benefits, and Metrics

Before the start of the migration, it was crucial to inform and educate all stakeholders of the upcoming change and addressing any concerns they have about the process. Feedback was collected and acted upon, generating new requirements to be implemented in the new platform.

The involvement and support from stakeholders at all levels ensured that the migration was adopted, rather than forced, which led to the successful and seamless migration of more than 5000 documents (after the culling of obsolete and redundant material), 80% of which were completed in the first week of the three weeks needed to fully migrate.

After the migration was completed, a significant improvement in product quality was noticed (over 20% according to the internal quality scores), which included higher quality localisation due to the integration of language needs throughout the product lifecycle.

The centralisation of knowledge across these many teams enabled the organisation to have consistent and clear messaging that was not possible before, which reflected in the positive reception of the new products that were released after the migration.

I. Problem

The rapidly evolving relationship between human expertise and artificial intelligence in the interpretation technology industry has generated the buzz-worthy catchphrase “Human in the mix” among language industry pundits and presenters, but what does it truly mean? —more importantly what should it mean? We need to map a clear vision for the advancement of AI tools and technologies within the interpreting profession.

II. Solution

• Defining “Human in the Mix”: Moving beyond the marketing jargon to articulate a meaningful role for interpreters alongside the release of AI tools.

• AI-Augmented Workflows: Examining how automation, machine learning, and real-time analytics can and will enhance interpreter performance and efficiency.

• Balancing Act: Addressing the concerns about over-reliance on AI and ensuring that the technology supports and empowers rather than replaces professional interpreters.

• Future Visions: What does an ideal partnership between interpreters and technology look like, and how do we think we can build together towards it?

III. Experiences, Benefits, and Metrics

Interpreting technology companies are developing technology solutions that respect and amplify human expertise while embracing AI as a powerful ally in the profession. There are already many demonstrable practical examples of how AI is being used today to enhance interpreter productivity without compromising the nuanced decision-making and cultural sensitivity that only human interpreters can and will continue to provide for the foreseeable future.

I. Problem

A tangled pipeline leads to confusion and delays.

SharkNinja had little experience of localization. International expansion hadn’t been a priority for them, and they didn’t yet appreciate its full complexity. SharkNinja prides itself on constantly iterating and evolving, but the lack of structure around localization was causing significant challenges.

Inconsistent handoffs of localizable assets and version control problems were causing inaccuracies and delays.

II. Solution

The gamechanger for Rubric was being granted access to SharkClean’s content repository. This transparency allows us to build an asset-based picture of how resources are evolving in real time, as well as allowing us to automate both the pull/push process and regression and validation checks.

Once we had established a shared Git repository to act as the source of truth for UI strings, we were able to design a branching strategy and connect the repo to our continuous localization framework.

III. Experiences, Benefits, and Metrics

Quote from Phoebe Zhang, Program Manager for Robotics at SharkNinja:

“Moving to Rubric’s process changed everything. It eliminated the confusion and chaos, and since then it’s been really smooth and efficient.”

– Typical turnaround time went from 14 -> 7 Days

– Development teams do not have to worry about “strings freeze” or “merge conflicts”. They can make UI label changes at any time. Any last minute strings changes are handled by MT and then checked by a linguist in the next sprint.

– Localization-readiness criteria are well defined and can be assessed when onboarding new component for localization.

– RubricCatcher automated QA analyzes content, highlights potential anomalies and out-of-sequence changes, checks terminology and improves consistency, resulting in more accurate translations.

– Finance administration was improved so that it does not affect our on-time project delivery.

I. Problem

The undergraduate localization program at Beijing Language and Culture University (BLCU) faces evolving challenges that reflect the dynamic demands of the global and domestic markets.

1. Parents seek programs with clear, forward-looking goals that align with cutting-edge career paths, finding traditional translator training programs less reflective of today’s technological landscape.


2. China’s young graduates are eager to seize opportunities in a competitive job market, where innovative skills are in high demand to support the nation’s rapid economic and technological advancements.


3. Global trade dynamics, including tariff fluctuations, underscore the growing need for skilled language professionals to advance China’s Belt and Road Initiative and strengthen international collaboration.


4. The rise of AI is transforming the traditional translation industry, prompting universities to rethink curricula to meet the urgent demand for tech-savvy language talent.

These challenges highlight the critical need for a modernized approach to language studies, which BLCU is proactively addressing.

II. Solution

In 2024, the contributions of BLCU’s localization graduates to the blockbuster video game Black Myth: Wukong attracted positive attention from administrators, who elevated the localization program and endowed it with a new name: AI Translation. This major is based on what we call Intelligent Language Studies, which harmonizes with LangOps principles.

BLCU Intelligent Translation Studies  ->  LangOps Principle

Human-centered  ->  Value human contribution

Tech-supported  ->  Try AI first

Knowledge-enabled  ->  Embrace data-centric AI

Scenario-driven  ->  Support all customer facing functions

In the AI Translation program at BLCU, we don’t just talk the talk, we walk the walk. Freshmen are enrolled in Python programming classes from day one. By the time they graduate, they have solid experience developing software, integrating AI into workflows via APIs, evaluating AI translation quality, managing databases, handling linguistic data, building content management systems, and vibe coding. We have fully transitioned from the value proposition of localization: delivering a product, to LangOps: developing intelligent language systems.

III. Experiences, Benefits, and Metrics

Thanks to our adoption of LangOps principles, the employment rate of graduates from the “AI Translation” program ranks #1 among the university’s student body of 22,340. Lintao Han’s and my contribution to the Routledge publication Translation Studies in the Era of Artificial Intelligence has attracted significant attention both in China and abroad. Translation programs at universities worldwide are ripe for change. LangOps is the template, and BLCU’s AI Translation program is the exemplar.

I. Problem

In April 2025, a 7.9-magnitude earthquake struck Myanmar, prompting China to deploy rescue teams as part of its commitment to global humanitarian aid. However, language barriers posed significant challenges. Myanmar primarily uses Burmese, with English in some regions, while Chinese rescue teams relied on Mandarin, with limited Burmese proficiency. This gap hindered critical tasks such as medical aid, resource distribution, and disaster assessment. For instance, rescuers struggled to interpret local injury descriptions or navigate Burmese place names, slowing response times. The urgency of the crisis demanded rapid, reliable language solutions tailored to disaster scenarios, yet traditional translation tools lacked the speed, adaptability, and multimodal capabilities needed for low-resource languages like Burmese.

II. Solution

To address this pressing need, Beijing Language and Culture University, in collaboration with the National Emergency Language Service Corps, developed an AI-powered Emergency Language Service Platform using Deepseek, a leading Chinese large language model. Completed in just 7 hours, the platform integrated text translation, speech translation, place name mapping, and image analysis, aligning with LangOps principles to deliver intelligent, scenario-driven language solutions.

The platform was built on Deepseek’s automated code generation, enabling rapid development of a lightweight, user-friendly mobile application. From day one, rescue teams and volunteers accessed real-time translations for Chinese, Burmese, and English, covering medical terminology, daily communication, and disaster-specific phrases. Speech translation, powered by integrated speech recognition and synthesis, facilitated on-site communication in noisy environments. The place name module, combining Deepseek’s translation with Google Maps, enabled rescuers to query Burmese locations and receive Chinese or English translations with coordinates. Image analysis identified critical details from disaster-site photos, generating multilingual descriptions to support decision-making.

This solution marked a shift from traditional translation—focused on delivering static outputs—to LangOps-driven intelligent language systems, dynamically supporting rescue operations through AI and data integration.

III. Experiences, Benefits, and Metrics

During the Myanmar earthquake, the platform provided over 5,000 translations, significantly enhancing rescue efficiency. For example, medics used speech translation to diagnose injuries in real time, while logistics teams relied on place name mapping to deliver supplies to remote areas. The platform’s offline mode and noise-resistant speech recognition ensured reliability in challenging conditions. Its rapid development and deployment underscored LLM’s potential as a cornerstone of emergency language services, earning praise from rescue teams and local communities. My related research has amplified global interest in LangOps-driven solutions. The platform’s success positions it as a model for future disaster scenarios, with potential applications in floods, typhoons, and public health crises, reinforcing China’s leadership in AI-driven humanitarian innovation.