16/04/2025

AI can save a lot of money in text production, customer service and translation. To make these efficiency promises a reality, the AI has to know the company and also have a precise understanding of the subject area and correct language use. Otherwise, errors and misunderstandings will quickly cancel out the time savings you hoped to make. Therefore, the data used to train AI systems and chatbots is crucial: it must be clean, structured and correct in order to deliver successful results. If you want to make long-term productivity gains using AI, having a solid, cleaned-up database is essential.

Comprehensive language data is a crucial building block for the successful deployment of AI systems and chatbots. Without high-quality data, even the most advanced models fall far short of their potential. When implemented and used correctly, the practical benefits are clear to see: companies that train their AI systems with well-prepared data or use the RAG (Retrieval Augmented Generation) approach benefit from more precise results, less need for corrections and significantly more efficient use of resources. The following examples illustrate how cleaned-up and structured language data can improve AI applications and what added value they can create for your company.

Recognising and using specialised terminology correctly: Imagine that an AI tool is tasked with translating a medical technology procedure document and comes across the term ‘haemophilia’. Without specialised language data, it could provide a generalised translation of this term in German, such as ‘Gerinnungsstörung’, meaning coagulation disorder. With correct medical terms in the data set, the bot not only recognises the term, but also knows that the technical term ‘Hämophilie’ is more appropriate here, and it can translate the information that follows appropriately.

Generating context-specific answers for concrete questions: A customer of a product asks: “How do I activate the energy-saving mode?” Without contextual data, a chatbot gives a generic answer, for example instructions for activating energy-saving mode using buttons that may not appear on the specific product. With data from product manuals, the AI can respond with precise instructions that match the product – in several languages, of course.

Offering multilingual support that takes cultural features into account: Several manuals are being created in different languages at the same time. The German system uses the term ‘Inbetriebnahme’, meaning commissioning, while the direct machine translation into English would suggest the phrase ‘taking into operation’. This sounds unnatural to native English speakers. That is why professional translators have previously used the term ‘commissioning’ and have stored it in the translation memory. The AI can now access this data and optimise the documents in any target language.

Ensuring consistent communication across different channels: A company refers to its main product in marketing materials as ‘SmartSolution’, while in the technical documentation it is referred to as ‘SS-2000’. Without standardised language data, AI-supported translation systems would not correctly associate the different terms with each other. This means that the chatbot would not find the right documents or answer questions correctly because it cannot make a connection between these terms. With clean terminology data – in this case by assigning the two product names as synonyms – the AI generates consistent and appropriate content across all materials. E-mails for potential new customers then contain the marketing name, the technical documentation contains the technical term, and all relevant materials are included in questions to the chatbot. This strengthens the company’s brand identity and prevents misunderstandings.

Translation memories (TMs) and terminology databases are key to unlocking these increased opportunities using company-owned data. These are built up over years in translation processes and contain precisely the valuable information that AI and chatbots need – in multiple languages. They represent not only corporate language but also the accumulated expertise that is essential for precise and helpful interactions with AI.

The fact that a company has a large amount of language data does not automatically mean that it can be successfully utilised for AI applications. Terminology databases and translation memories grow continuously with each translation project, but they also need to be cleaned up and updated regularly. In practice, this often falls by the wayside. This is why the language data in TMs and terminology databases, accumulated over many years, often contains:

• Obsolete terms and product names
• Inconsistent translations of identical segments
• Incorrect segmentation or fragments
• Contradictory definitions and instructions
• Duplicate entries with different information

This means that the AI system is trained with contradictory, ambiguous or irrelevant data, which can significantly impair the quality of the output. The billing models of many AI applications are also based on tokens, so processing redundant or incorrect data incurs unnecessary costs.

Making language data truly usable for AI and chatbots requires a systematic approach to cleaning up and preparing data alongside using TMs and terminology databases. The following steps have proven to be particularly effective in practice:

The first step is to systematically analyse the existing language data. The following aspects should be considered:

• The scope and structure of existing translation memories and terminology databases
• Duplicates and contradictory entries
• The proportion of content that is outdated or no longer relevant
• Consistency across different languages and document types

A detailed analysis provides a clear picture of what needs to be cleaned up and enables a realistic assessment of the costs involved. Modern tools such as oneCleanup provide assistance with their automated analysis capabilities.

The analysis is followed by the actual clean-up. This typically includes:

• Removing or merging duplicate entries
• Updating outdated terminology
• Standardising inconsistent translations and terms
• Adding missing information, especially for key terms
• Correcting incorrect segmentation

Ideally, this process should be carried out using a combination of automated tools and human expertise to ensure both efficiency and quality.

Cleaned-up language data must then be prepared to make it as suitable as possible for AI training processes:

• Categorising data according to subject areas or product lines
• Labelling data according to how current and relevant it is
• Creating specific glossaries for certain areas of application
• Defining clear hierarchies where there is competing terminology information

For machine translation, for example, it is clear that not all terminology entries are equally relevant. Intelligent prioritisation can prevent the AI application from being restricted in its performance by too many specifications.

Maintaining language data is not a one-off project, but an ongoing process. Continual quality assurance is essential, especially for AI applications that are regularly trained with new data. This involves:

• Implementing clear processes for adding new language data
• Regularly reviewing and updating key terms
• Setting up feedback loops between AI usage and language data maintenance
• Implementing automated quality checks for new data

Systematically cleaning up, structuring and preparing language data may initially seem like an additional expense, but the investment pays off in many ways. Companies that make a point of maintaining their databases not only create the basis for successfully implementing AI, but also achieve measurable benefits when using it day to day.

1. Improved AI performance: Chatbots and AI tools provide more precise, contextualised and helpful answers.
2. Cost efficiency: In token-based AI models, using cleaned-up data significantly reduces processing costs. Clean TM data can also lead to significantly lower translation costs, even for human translations.
3. Consistent communication: Standardising the use of terminology strengthens a company’s brand image across all communication channels.
4. Multilingual excellence: High-quality language data enables excellent AI interactions in all the company’s languages.
5. Scalability: With a solid foundation of clean language data, AI applications can be more easily expanded to new subject areas, languages or markets.

The increasing importance of AI and chatbots makes language data a strategic resource that extends far beyond the original context of translation. Companies that systematically clean up, structure and maintain their language data create the basis for successfully implementing AI and securing a competitive advantage.

Would you like to fully utilise the potential of your language data for AI applications? Our experts will analyse your translation memories and terminology databases and work with you to design a customised data clean-up strategy. We’ll be happy to provide a consultation.