In a recent conversation with our CTO, Peter Bukhave, we talked about why AI translation is far more complex than simply prompting an LLM.
Who hasn’t tried an AI tool and received a fairly satisfactory result?
But when you then prompt it again to refine, the LLM suddenly reworks everything, changing what actually worked in the first place and keeping the things that didn’t work!
And that’s within the same language. Even then, it can completely rewrite the text, changing wording, tone, terminology, or structure.
Each context needs its own AI model
Once translation enters the mix, it becomes even more complex. Some outputs improve, others become less accurate, and hallucinations can occur between languages. On top of that, what counts as an “acceptable” translation depends entirely on the context.
A legal team, marketing department, support organisation, and manufacturing division may all need completely different outcomes from AI translation. That often means different terminology rules, governance models, and AI models trained for different use cases.
A company might translate into 80 languages across multiple content types, markets, and departments. Suddenly, you’re managing enormous numbers of language pairs, workflows, outputs, and AI configurations.
How many models are you suddenly maintaining?
The models themselves also change very quickly. Your business goals can change too. Maybe you want a different brand tone, a different market focus, or a different language pair.
How to succeed with AI localisation at scale
Soon, the challenge is no longer just “Can AI translate this?” but: Can we maintain, govern, optimise, and scale this cost-efficiently over time?
That’s why it’s so important to:
Configure AI models for each context, such as market, language pair, or department
Continuously maintain and update them as requirements evolve
Stay up to date with the underlying AI technology itself
According to Peter Bukhave, one of the biggest pitfalls is building something quickly that becomes impossible to maintain afterwards.
Bridging translation, technology, and human expertise
AI is here, everywhere, and moving fast. That’s why we’ve built it into the core of our offering in a way that allows us to create AI models tailored to each customer’s specific context. We typically start by establishing the same foundation across language pairs, then learn from the data and optimise per language pair and customer over time.
At LanguageWire, we bridge translation, technology, and human expertise, so customers never have to worry about whether their AI workflows are going to break tomorrow.
If you’d like to build AI models that work for your teams, markets, and language pairs, our experts are ready to help.
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