Hansem AI Translate’s listing in the memoQ Ecosystem reflects a broader approach at Hansem Global: when existing MT and AI tools leave gaps in real translation workflows, Hansem builds technology to close them. The goal is practical—better quality, control, and scalability in production.
Why Would a Language Service Provider Build Its Own memoQ Plugin?
Hansem Global has used memoQ as a core translation production environment for many years. As leveraging AI based pre-translation became more popular practice, the next step was to integrate AI to existing workflows without giving up translation memories, termbases, QA processes, and other production assets.
The limitation became clear in real projects. LLMs evolve quickly, and different models can perform differently by language pair, domain, and writing style. When the available AI options are limited, teams cannot always move quickly to a newer model or select the model that performs best for a specific project.
That difference really matters in producing high-quality translation outputs. In Hansem Global’s frequent language pairs such as English-to-Korean, Japanese, Chinese, and Vietnamese, differences in first-pass AI translation quality can translate directly into differences in downstream post-editing effort.
Hansem AI Translate was built to address that constraint. It is not a proprietary MT engine. It is a bring-your-own-key connector that lets memoQ users connect directly to OpenAI, Anthropic Claude, or Google Gemini with their own API credentials. Because the workflow is not tied to a single model, teams can change LLMs by project or domain without switching the translation environment or TMS.
Translation data also does not pass through an intermediary Hansem’s server. Segments are sent directly to the LLM provider selected by the user under that user’s own account and data terms.
The plugin is not specific to Asian languages; it can be used across the language set supported by the memoQ SDK. Hansem first deployed it in its own production environment and now uses it across workflows in Korea, the United States, Vietnam, and selected partner’s systems. It is also available at no charge to memoQ users who request it. Hansem AI Translate is listed as a verified Plugin in the memoQ Ecosystem.
AI Translation Technology Starts with Production Problems
Hansem Global generally follows the same pattern when it develops language technology. It does not start with a technology and look for a place to use it. It starts with a production problem that existing tools can not resolve.
At scale, translation quality depends on more than the quality of individual translated segments. Teams need to manage customer terminology and style, detect recurring issues across many languages, track changes and omissions across large file sets, and apply project-specific quality controls consistently.
To address those needs, Hansem has developed a range of QA and customer-specific production tools. The purpose is not to remove linguistic judgment, but to automate repetitive checks, surface likely error points earlier, and give language specialists better information for review.
Hansem CALM (Computer-Assisted Linguistic Model) grew out of the same production need. The in-house language AI platform connects AI quality evaluation, terminology candidate extraction, AI translation, and human post-editing within a single workflow, so AI can be managed as part of the production process rather than as a stand-alone feature.
In Hansem’s experience, model choice matters, but so does where the technology is inserted into the workflow, how automated output is validated, and how each customer’s quality requirements are translated into operational checks. Hansem AI Translate is one expression of that broader approach.
Asian-Language Challenges Extend Beyond Text Translation
The same production-driven approach applies to media localization.
AI-based subtitling and dubbing tools have improved rapidly, but Hansem found that commercial tools could still require substantial manual correction in English-to-Korean and English-to-Japanese projects. Korean and Japanese differ from English in sentence structure and information order, and the spoken duration needed to express the same meaning can also change after translation.
In text translation, those differences can often be handled naturally. In subtitles, they affect segmentation, reading length, and what fits on screen. In dubbing, they affect synchronization between the translated speech and the video. If people still need to re-segment subtitles, compress sentences, or adjust speech duration extensively after the AI step, the efficiency gain can be limited.
That was the starting point for Hansem’s in-house AI Media Solution. Its origin is different from Hansem AI Translate. The memoQ plugin was created to give production teams more freedom to select and connect LLMs. The media solution was developed to reduce language-specific production friction in subtitling and dubbing, particularly for Korean and Japanese.
The common principle is not that every problem needs a proprietary tool. It is that different production problems require different technical responses.
Large-Scale Asian-Language Programs Need Operating Depth
Strong linguists remain essential, but large multilingual programs also depend on the operating system around them: terminology and style management, in-market quality review, QA, production handoffs, and increasingly, multimedia localization.
This is especially relevant when English-centered processes and tools are extended to Korean, Japanese, Chinese, Vietnamese, and other Asian languages. The same workflow does not always behave the same way across languages, so production teams need both linguistic depth and the ability to adapt the process when necessary.
Hansem Global is headquartered in Korea and operates Asian-language production from the region, with a multilingual production hub in Vietnam. Its U.S. office supports North American customers and project management. This structure allows Hansem to run global multilingual programs while keeping production closer to the markets and language teams responsible for key Asian languages.
Hansem Global’s operating model is designed to support global language programs while maintaining deeper production proximity for Korean, Japanese, Chinese, Vietnamese, and other Asian languages. That combination – global program management, Asian-language depth, and in-house localization engineering – is central to how the company approaches translation and localization work.
Where Commercial AI Translation Solutions Still Leave Gaps
Commercial translation platforms, AI translation solutions, and automation tools continue to improve. Hansem does not take the view that every language service provider should build its own technology. When an existing solution works well, using it is usually the most efficient choice.
But live production still produces edge cases that standard features do not always solve: recurring errors in a specific customer’s content, language-specific QA issues, restrictions on model choice, or timing and segmentation problems in Korean and Japanese media localization.
Hansem’s response is pragmatic: use existing technology where it works, connect systems when needed, build only where a meaningful gap remains, and validate the result in real translation and localization production.
The memoQ Ecosystem includes MT engines, AI translation solutions, and integration technologies from a range of providers. Hansem AI Translate now sits within that ecosystem as a tool that originated inside an LSP’s own production environment.
For Hansem Global, the plugin is not a separate software product created primarily for sale. It is production technology built to solve a translation problem and still used in day-to-day work.
Its memoQ Ecosystem listing is therefore one example of how Hansem combines Asian-language expertise, global program operations, and localization engineering to solve practical problems in language production.