ChatGPT, Claude, Gemini—today’s large language models (LLMs) show impressive translation performance. But anyone who has tried using these models on real business documents tends to encounter the same issue: everyday sentences may look excellent, but quality becomes unstable as soon as industry-specific terminology appears.
This is not a defect in AI. It is a structural limitation of general-purpose models. And there are ways to overcome it.
Why general-purpose AI struggles in specialized translation
General-purpose LLMs are trained on vast amounts of publicly available internet text. As a result, they have strong general language ability, but they are not always able to handle the terminology, style, and context of a specific industry with sufficient precision.
For example, when translating the term “torque converter” in an automotive service manual, a general-purpose AI may produce different equivalents depending on the context. It does not know which term matches that client’s approved terminology. In medical documents, the same drug name may be rendered inconsistently across sections. In legal documents, subtle logic in a conditional clause may shift in meaning.
In a one- or two-page document, these issues may seem minor. But in projects involving tens of thousands of words, repeated inconsistencies like these can undermine the overall quality of the translation.
The solution: giving AI domain expertise
There are two main ways to address this problem.
The first is to have the AI reference glossaries and past translation data during the translation process. This has the advantage of using existing assets immediately without training a separate model. However, its impact is limited when the reference data is incomplete or insufficient.
The second is to tune the model itself using domain-specific data. Because the model internalizes the language patterns of that field, this approach can deliver a more fundamental improvement in quality. The challenge is that it requires a sufficiently large body of high-quality domain data.
Hansem Global is pursuing both approaches. Through TM integration and terminology reference within AI Workstation, we can improve quality immediately. At the same time, we are advancing toward domain-specific model development as a longer-term way to improve the underlying precision of AI translation.
What data is used to build a domain-specific model?
At this point, a natural question arises: “If you are using domain data, doesn’t that ultimately mean using customer data?”
No. That point should be made clear.
Hansem Global’s approach is not to place the client’s original data directly into model training. Instead, domain-level language patterns and specialized knowledge accumulated through more than 20 years of translation projects across industries such as electronics, IT, and automotive are being systematically organized for use in AI model development, and reconstructed into synthetic data for model tuning.
A useful analogy is this: when an experienced translator spends ten years translating automotive manuals, what that person accumulates is not a client’s confidential document content. What they accumulate is domain-level expertise — an understanding of which expressions are natural in that field and which terms are accurate. That is the expertise Hansem Global aims to reflect in a domain-specific AI model.
What clients are truly concerned about is not whether their data is referenced in a process, but whether it can be identified and traced back to them elsewhere. Through synthesis and reconstruction, source data is transformed into domain-level language patterns, and nothing remains in a form that can be attributed to any specific client. This is the principle that governs how Hansem Global handles data.
Going further: the possibility of customer-dedicated models
If a domain-specific model improves translation quality across an industry, the next step is a model dedicated to an individual client.
For clients that work with Hansem Global on an ongoing basis, it becomes possible to train a dedicated model using that client’s translation data as the foundation, supplemented by synthetic data to broaden the model’s coverage. This data is used exclusively for that client’s dedicated model and is never mixed into other clients’ projects or general-purpose models.
The value of this approach is clear.
Quality improves as the partnership continues. The dedicated model becomes increasingly precise in reflecting that client’s terminology, style, and domain characteristics. At the beginning, AI translation may still require substantial post-editing. But as the dedicated model becomes more refined, high-quality output can be achieved with less manual correction.
The cost structure also improves over time. As the scope of post-editing decreases, project costs decrease as well. This is not just a matter of “AI made it cheaper.” It is a structure in which efficiency increases in proportion to the length and depth of the working relationship.
Hansem Global is actively advancing in this direction. The work is still in the development and validation stage, but because we already operate our own AI Workstation platform, the technical foundation for this kind of expansion is already in place.
Why not everyone can do this
Domain-specific models and customer-dedicated models may sound straightforward in concept. But in practice, two conditions are required.
The first is high-quality domain data. It cannot be general web data. It must be data that has passed through real professional translation workflows and expert review. This kind of data cannot be built overnight. The data Hansem Global has accumulated across industries over the past 20 years is not easily replicated.
The second is a proprietary technology platform. In an environment that depends on a commercial translation platform, it is structurally difficult to integrate custom models or adapt workflows at the customer level. Because Hansem Global operates AI Workstation as its own platform, we can develop domain-specific models and connect them directly to translation, review, and post-editing workflows.
Not many translation providers have both the data and the platform.
From general-purpose to domain-specific, and from domain-specific to customer-dedicated
The future of AI translation is not just about improving the performance of general-purpose models. Real competitive advantage comes from layering industry expertise and customer-specific adaptation on top of strong general language capability.
Hansem Global is advancing a step-by-step roadmap: using general-purpose LLMs, developing domain-specific models, and ultimately building customer-dedicated models. Throughout that process, customer data security and ownership remain strictly protected.
If you expect AI translation to go beyond being “good enough” and become truly accurate for your industry, it is worth discussing what makes that difference with Hansem Global.
This is the third article in our series, How to Adopt AI Translation the Right Way.
Continue reading:
Part 1. Where Should You Start with AI Translation?
Part 2. Why Do Results Differ So Much If Everyone Uses AI Translation?
Part 4. Is AI Translation Effective for Every Project?
Part 5. Where Do AI Translation Quality Failures Happen, and How Can You Prevent Them?
Part 6. What You Must Check Before Adopting AI Translation