Beyond AI

AI can translate.
Expertise makes it work.

Let’s address the obvious question: with all these powerful AI tools available, why would you still need a localization specialist to translate your content?

AI has made translation faster and more accessible. I use it when it genuinely improves a project—but not as a substitute for understanding your product, your audience or your brand.

A translation can be fluent and still be wrong.

It may choose the wrong meaning of a short interface label, add information that was not present in the original, introduce terminology that Italian users would never choose or produce wording that sounds perfectly plausible until you see it inside the product.

This is not just a theoretical concern. Research has shown that even advanced machine-translation systems can generate unsupported content, while detecting those errors automatically remains challenging.^[1] Other studies have found a persistent gap between an AI model recognizing cultural knowledge and applying it appropriately in a translation.^[2]

That is where a localization specialist makes the difference.

Context, not guesswork

Words rarely appear in isolation.

A button, notification or product description can have several valid translations depending on where it appears, what the user is doing and what the product is supposed to communicate.

I review content in context, identify ambiguity and ask the questions needed to make an informed decision.

One consistent Italian voice

Your customers should not feel as though your website, product and support content were written by different people—or by different machines.

AI can be given terminology and brand instructions, but it still needs oversight to ensure that it follows them consistently across different formats, releases and communication channels.

I help define and apply the right terminology, level of formality and tone of voice across your product, website, documentation, support content and marketing materials.

Adaptation, not word replacement

Italian customers do not need English content with Italian words.

They need instructions that are immediately understandable, product copy that sounds credible and campaigns that create the same effect as the original—even when achieving that effect requires changing the wording.

Localization is not only about what the original words mean. It is about what the final content needs to achieve.

Judgment and accountability

AI can suggest an answer. It cannot take responsibility for whether that answer is right for your product and your customers.

Automated quality scores are useful, but their presence does not guarantee that every important error has been found. TAUS, an independent language-industry organization, warns that automated quality estimation can create a false sense of confidence when its results are not tested against human decisions and real errors.^[3]

I review meaning, terminology, tone, cultural relevance and user experience. I flag potential problems, explain important decisions and take responsibility for the quality of the Italian content before it reaches your audience.

The right process for the right content

Not every project needs the same approach.

High-volume or repetitive content may benefit from AI-assisted translation followed by focused expert review. Product launches, interface content, brand campaigns and sensitive communications may require closer human involvement from the beginning.

My role is not to use as much AI as possible—or to avoid it at all costs.

It is to choose the most appropriate combination of technology and human expertise for your content, audience, budget and quality expectations.

You get the efficiency of modern tools, combined with the judgment of a native Italian specialist who understands your product.

AI is part of my toolkit.
Your Italian users are my responsibility.

Let’s find the right approach for your content

Selected research

[1]
Machine Translation Hallucination Detection for Low and High Resource Languages Using Large Language Models
Benkirane et al., Findings of EMNLP, 2024.
Research into unsupported or invented content in machine-translated output and the challenges involved in detecting it.
[2]
Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation
Yuan et al., Association for Computational Linguistics, 2026.
The study identifies a continuing gap between recognizing culture-specific knowledge and applying it correctly in translation.
[3]
Quality Estimation Is Not Solved. It's Just… Included.
TAUS, 2026.
An industry analysis of the limitations of automated translation-quality estimates and the risk of relying on them without validation.

AI can translate.Expertise makes it work.