AutoTranslation: When AI Translates Product Descriptions Into Multiple Languages – to a Professional Standard

AutoTranslation: When AI Translates Product Descriptions Into Multiple Languages – to a Professional Standard

Product descriptions are the voice of a brand in digital retail: they inform, persuade and sell – across online shops, marketplaces and catalogues. For internationally active brand manufacturers, however, these texts must not only be created once, but must also be available in many languages: consistent, true to the brand and readily accessible. This is where the translation of product descriptions becomes a bottleneck.

And this is precisely where Advellence’s AutoTranslation comes in.

The AI-powered application translates product texts from a source language into over ten target languages – whilst taking into account brand language, specialist terminology and the respective text type. The solution has been specifically refined through several iterations, successfully validated for various language combinations and is already in productive use.

The Challenge: Multilingual Product Texts on a Large Scale

A brand manufacturer in the consumer goods sector often manages several thousand products – each with multiple text elements such as product name, claim, e-commerce headings and detailed description. Multiplied by a dozen target languages, this results in a translation volume that is virtually impossible to manage manually.

Traditional approaches reach their limits here. Pure human translation delivers high quality, but is slow and expensive. Generic machine translation is fast, but understands neither brand context nor specialist terminology. The English word ‘horn’, for example, can refer to a car horn – or, in the context of high-quality knives and tools, the handle material made from animal horn. Anyone unfamiliar with the context will translate it incorrectly. And it is precisely these subtleties that determine whether a product text comes across as professional or not.

The Solution: AI Translation With Brand Knowledge and Human Oversight

AutoTranslation combines the speed of AI with the brand’s specialist knowledge. Instead of simply translating texts word for word, the application operates within clearly defined guidelines:

Terminology control via glossaries: Brand-specific terms are translated consistently and with the correct meaning. Thus, ‘nut’ is reliably translated as a nut (the fastener) – not the fruit.

Attribute-specific logic: A product name is treated differently from a marketing text or a technical description.

Learning from approved translations: Previously approved, human-translated product texts serve as examples and shape the style and tone.

Crucially, technical oversight remains with humans. Translation suggestions are checked and approved before going live. The AI relieves the burden of repetitive routine work – whilst responsibility for the brand remains in-house.

Quality That Can be Measured

A good translation result cannot simply be guessed at – it can be verified. Every translation is automatically compared with professional, human-produced reference translations. This makes quality transparent and allows progress to be tracked across multiple rounds of optimisation.

One point is crucial here: particularly with marketing and free-form texts, there is rarely just one ‘correct’ translation. The same message can be phrased in many equally apt ways – and the human reference is just one of them. If the AI deviates from this single reference, it is therefore usually not an error, but an equally valid linguistic variant. A high degree of correspondence confirms the quality; a deviation does not refute it.

The application has been specifically improved over several rounds of optimisation.

Serious mistranslations – that is, cases where the meaning is actually distorted – have been reduced by around four-fifths and are now the rare exception. The result is a system that is not only fast but also reliable – and whose quality can be verified.

Further functional components include:

·      Glossary and terminology control as guidelines

·      Attribute-specific translation logic

·      Few-shot learning from approved reference texts

·      Continuous, measurable quality control

·      Human review and approval prior to deployment

AI in Production Requires More Than Just a Good Model

The journey from an AI idea to a productive application requires far more than simply choosing a language model. Terminology, context, measurable quality and integration into the existing system landscape are crucial.

AutoTranslation is therefore designed to be model-independent: the application can be operated with different AI providers and is not tied to a single model manufacturer. This protects against dependencies and makes it possible to choose the right model for every task and every budget – at a fraction of the cost of traditional translation services.

Rather than functioning as an isolated tool, the translation is integrated into the very processes where product data is created and maintained: within the PIM and data management processes. The architecture, implementation and quality assurance were carried out entirely in-house by Advellence – as was the expertise required to set up and operate productive AI translation in an enterprise environment.

Relevance for PIM, E-Commerce and Internationalisation

Multilingual product texts are a key component of any internationalisation and e-commerce strategy. The faster and more reliably new products are available in all relevant languages, the quicker markets can be tapped into, channels served and product ranges scaled up.

AI creates real added value here when it is not viewed in isolation, but is integrated into existing data, process and system landscapes. AutoTranslation demonstrates how translation quality, speed and control can be combined – with less manual effort and greater process reliability.

Outlook: From Translation to Intelligent Text Processing

Further development of AutoTranslation is focusing on additional areas of application throughout the product text creation process. Plans include the automatic enrichment and validation of glossaries directly from approved translations, a translation memory for reusing previously translated texts, and the AI-supported creation of product and channel texts – from descriptions to marketplace-specific listings.

In this way, AutoTranslation is gradually evolving into an intelligent building block for multilingual, data-driven product communication.

Conclusion

AutoTranslation demonstrates how AI can be effectively deployed in an enterprise environment: in a practical, measurable way and with clear human oversight.

The focus is not on the technology alone, but on the operational added value. Where product texts still have to be laboriously translated into many languages today, AI can provide targeted support – and help companies make their internationalisation faster, more consistent and more cost-effective.

Experience AutoTranslation Live

Would you like to know how AI can specifically support the multilingual creation and translation of product texts?

On 22 September, we’ll be showcasing our AI applications at Advellence Connect:s in Bielefeld in a live demo. Places are limited and will be confirmed by Advellence upon registration.

Register now and watch the live demo

Or get in touch with us directly: https://advellence.com/en/kontakt