Frequently asked questions about ownvia.

AI translation refers to the automated translation of texts using artificial intelligence.

Unlike older translation systems, modern AI analyses not only individual words but the entire context of a document. This results in more natural and accurate translations. In professional business processes, AI is additionally combined with terminology, translation memories and company knowledge to achieve consistent, high-quality results.

Benefits for companies:

• Efficiency: Large volumes of text can be translated in a short time.
• Scalability: Multilingual content can be processed efficiently even when translation volumes are high.
• Higher quality: Terminology, corporate language and existing translation knowledge can be taken into account in a targeted manner.

AI translation analyses the entire content of a text and creates a context-based translation from it.

Unlike older translation systems, modern artificial intelligence evaluates the context of individual sentences, recognises linguistic patterns and takes style, grammar and meaning into account. This results in considerably more natural translations.

Professional AI solutions also supplement this process with company knowledge such as terminology, translation memories and style guides.

This enables:

• Context-based translations: Words are translated appropriately for the context.
• More natural language: Formulations sound more fluid and are easier to understand.
• Greater consistency: Company knowledge is incorporated directly into the translation process.

Further reading

How do AI translations work?

Find out how modern AI translations analyse context and why company knowledge is crucial for high-quality results.

→ Read the specialist article

AI translation helps companies make translation processes faster, more cost-effective and more efficient.

Especially for regularly updated content or large volumes of documents, artificial intelligence significantly reduces the manual translation effort. At the same time, content can be published in multiple languages more quickly.

Companies benefit from:

• Lower translation costs: Recurring tasks are automated.
• Faster time to market: Multilingual content is available more quickly.
• High scalability: Even large translation volumes can be processed efficiently.

Further reading

 AI integration: Why individual AI tools are not enough

Find out why companies should not use AI in isolation and how professional AI processes are structured.

→ Read the in-depth article

Modern AI translation solutions can automatically translate almost all common business documents.

These include technical documentation, user manuals, contracts, marketing materials, websites, product information, software texts and internal documents, among others. Most professional solutions support numerous standard file formats and largely preserve the original layout automatically.

Commonly supported file formats include, for example:

• Microsoft Word (.docx)
• Microsoft Excel (.xlsx)
• Microsoft PowerPoint (.pptx)
• PDF
• HTML
• XML
• XLIFF
• SDLXLIFF
• JSON
• CSV

This offers businesses:

• Broad compatibility: Most common document and exchange formats can be processed directly.
• Efficient workflows: Existing documents do not need to be manually reformatted before translation.
• Seamless integration: Existing translation projects and CAT tool formats can often continue to be used.

Further reading

Find out how companies can translate complete websites professionally and in a search-engine-optimised way into multiple languages.

→ Read the specialist article

Traditional machine translation translates texts directly from the source text into the target language. Modern AI translation goes beyond this by understanding relationships, taking context into account and adapting the writing style specifically.

This results in more natural and precise translations. In professional translation processes, AI is also combined with company knowledge such as terminology, translation memories, style guides and reference documents. This produces translations that are not only linguistically correct, but also technically accurate and compliant with the brand.

This offers companies:

• Greater understanding of context: AI takes the context of an entire document into account instead of considering individual sentences in isolation.
• Higher translation quality: Terminology, corporate language and style guidelines are incorporated directly into the translation.
• Less post-editing: More precise results reduce the effort required for quality assurance.

Further reading

Find out what differences exist between traditional machine translation, large language models and modern AI platforms.

→ Read the expert article

Professional AI translation solutions use glossaries and terminology databases to ensure that defined specialist terms are used consistently across all translations.

This means that, for example, product names, technical terms, brand names or industry-specific terms are automatically recognised and translated in accordance with the specified terminology rules. This ensures terminological consistency even in large translation projects.

These include, among others:

• Product names
• Technical terms
• Brand names
• Abbreviations and specialist terms
• Do-not-translate terms (terms that must not be translated)

This offers companies:

• Consistent terminology: Approved terms are used consistently.
• Higher translation quality: Specialist errors and inconsistent translations are reduced.
• Stronger brand communication: Product and company terms remain consistent across all languages.

Further reading

AI-powered terminology extraction: The complete guide for businesses

Find out how companies can automatically capture specialist terms and use them permanently for AI translations.

→ Read the specialist article

Company knowledge ensures that AI not only translates correctly from a linguistic perspective, but also takes company-specific terms, product names and wording into account.

This includes, for example:

• Translation Memories
• Glossaries
• Style Guides
• Reference documents
• Websites
• Previous translations

This results in significantly more consistent output.

No. AI does not replace professional translators; instead, it supports them in the translation process.

Modern AI produces high-quality pre-translations within a few seconds and handles many recurring tasks. Experienced specialist translators then review terminology, cultural nuances, style and industry-specific requirements. This process – known as post-editing – ensures that translations meet professional, linguistic and company-specific quality requirements.

Especially for specialist texts, contracts, marketing content or sensitive company information, human expertise remains indispensable.

This offers companies:

• Higher efficiency: AI shortens processing times and reduces the manual translation effort.
• High translation quality: Specialist translators ensure correct terminology, style and technical accuracy.
• The optimal combination of humans and AI: Speed and automation are combined with human expertise and quality control.

Further specialist article

The translation quality with ownvia is really good. Customer feedback from practice

Find out why AI and human quality assurance together achieve the best results.

→ To the specialist article

Yes – provided that the AI translation solution used meets the requirements of the GDPR.

It is not artificial intelligence itself that is decisive, but how company data is stored, processed and protected. Particularly when dealing with confidential content, companies should ensure a transparent data protection strategy and secure infrastructure.

What companies should look out for:

• No AI training with customer data: Company data should not be used to train public or external AI models without consent.
• Server location within the EU: Ideally, processing should take place in European data centres or under demonstrably GDPR-compliant conditions.
• Data Processing Agreement (DPA): The provider should provide a legally compliant Data Processing Agreement.
• High security standards: Encrypted data transmission as well as a role and permission management system help to effectively protect confidential company data.

Further reading

Making AI translation controllable: ownvia the engine of your internationalisation

Find out why data protection, company knowledge and European infrastructure are crucial for professional AI translations.

→ Read the specialist article

The quality of an AI translation is ensured through a combination of automated quality assurance checks (Quality Assurance, QA) and human post-editing.

Professional translation processes rely on several quality assurance measures. Automated QA checks identify technical and formal errors during the translation process. Specialist translators then review terminology, context, style and technical accuracy to ensure a high quality of translation.

The key pillars of quality assurance:

• Automated QA checks: Checking terminology, numbers, dates, spelling and technical tags, for example in HTML or XLIFF files.
• Human post-editing: Specialist translators review the tone, context, style and technical precision of the translation.

Benefits for companies:

• Higher translation quality: Automated and manual checks complement each other perfectly.
• Less reworking: Errors are identified at an early stage and corrected efficiently.
• Reliable compliance with company standards: Terminology, style guides and quality guidelines are consistently taken into account.

Further reading

Checking Translation Memories with AI: How to keep pre-translations up to date

Find out why regularly maintaining Translation Memories sustainably improves the quality of AI translations.

→ Read the specialist article

ownvia combines AI with company knowledge, translation memories, terminology, style guides, quality checks and professional translation processes.

Instead of translating texts exclusively automatically, ownvia helps companies intelligently integrate existing knowledge into the translation process. Translation memories, glossaries, reference documents, style guides and individual AI prompts work together seamlessly. This creates traceable, consistent translations that can be integrated into existing workflows and scaled over the long term.

This offers companies:

• Higher translation quality: Company knowledge flows directly into every translation.
• Consistent corporate language: Terminology, style and brand communication remain consistent across all languages.
• Less post-editing: More precise translation suggestions reduce the manual review effort.
• Scalable translation processes: AI-supported workflows can be efficiently integrated into existing business processes.

Further reading

Rethinking TMS: Advancing existing translation processes with AI

Find out how ownvia expands existing translation processes instead of replacing them.

→ Read the article

AI translation helps businesses provide multilingual content more quickly, efficiently and consistently.

Combined with business knowledge, terminology, translation memories and human quality control, AI translation is becoming an important part of modern translation processes. Businesses can manage international content efficiently while ensuring that their corporate language and quality standards remain consistent across all markets.

The key benefits at a glance:

• Shorter time to market: Multilingual content can be created and published more quickly.
• Lower translation costs: Automated processes reduce manual effort and increase efficiency.
• Higher translation quality: Business knowledge, terminology and translation memories ensure consistent results.
• Scalable international communication: Even large translation volumes can be managed centrally and efficiently.

A Translation Memory is the digital memory for your translations.

Every sentence that has already been translated is stored and can be automatically reused in future projects. This enables companies to benefit from:

• Greater consistency – Consistent specialist terminology and corporate language across all documents.
• Higher efficiency – Recurring content does not need to be translated again.
• Lower costs – Already translated content is used multiple times.

A translation memory recognises content that has already been translated and automatically suggests it again.

For every translation project, the system compares new texts with the existing translation database. If identical or similar segments are found, they can be adopted directly or used as a suggestion.

This results in:

• faster translation processes
• consistent wording
• less manual translation effort

Yes. Existing Translation Memories can generally be reused without any problems.

Most professional translation systems support importing existing translation data, for example in TMX format.

For companies, this means:

• existing translation knowledge is retained
• no duplicate work
• a faster start to new translation projects

A translation memory ensures that your translation knowledge remains within your organisation and becomes more valuable with every project.

The larger your language repository grows, the greater the benefits become:

• Cost savings at the touch of a button: The more text is repeated in subsequent projects, the lower the translation costs become.

• Maximum scalability: Even very large volumes of text and international launch projects can be completed in a fraction of the time.

• Full data sovereignty: Your translation knowledge remains securely in your hands – regardless of which service providers or translators you commission.

• A consistent brand voice: Customers and partners experience a consistent and professional corporate language worldwide.

Most modern translation solutions support the common formats for importing and exporting translation memories.

The most commonly supported formats are TMX files (Translation Memory eXchange), Excel files (XLSX), CSV files and the native formats of many CAT tools.

For cross-system exchange, TMX (Translation Memory eXchange) has established itself as the international standard.

As a result, companies benefit from:

• Vendor independence: Translation memories can be exchanged easily between different translation systems.
• Investment protection: Translation data that has already been built up is retained in the long term and can continue to be used.
• Easy migration: Switching to a new translation solution is possible without losing valuable translation knowledge.

A translation memory provides the AI with proven translations as a reference.

Instead of reformulating every sentence from scratch, the AI draws on previously approved translations. This improves linguistic consistency and ensures that the company’s language and specialised terminology are followed reliably.

This results in:

• more consistent translations
• higher-quality AI results
• less manual correction effort

A translation memory should be reviewed and cleaned up regularly, as well as after major changes within the company.

Over time, outdated technical terms, duplicate entries or incorrect translations may arise. Typical occasions for a clean-up include terminology updates, rebrandings, new product generations or software relaunches. Regular maintenance ensures that translators and AI systems access only up-to-date and approved translations.

Regular clean-up ensures:

• Up-to-date translation suggestions: Outdated terms and former product names are removed from or updated in the translation memory.
• Higher translation quality: Translators and AI access consistent and reviewed reference translations.
• Maximum consistency: Conflicting translation variants (multiple matches) are reduced, keeping the company's language consistent.

A Translation Memory and a glossary serve different purposes and complement each other perfectly in the professional translation process.

The key difference lies in the information they store:

Translation Memory (TM): Stores complete sentences or text segments, including their translations. This allows recurring content to be automatically identified and reused.

Glossary (terminology database): Contains individual technical terms, product names, abbreviations and their approved translations. It can also define preferred terms or terms that must not be translated (Do-Not-Translate).

Together, both tools ensure:

• Consistent terminology: Technical terms are used correctly and incorporated into consistent translations.
• Higher translation quality: Translators and AI systems reliably follow terminology and language requirements.
• More efficient translation processes: Fewer queries, fewer corrections and consistently high translation quality.

Matches are created by a translation memory automatically comparing new sentences or text segments with translations already stored.

For each new segment, the system checks whether identical or similar content is already available in the translation memory. The higher the match, the more suitable the translation suggestion. This means existing translations can be reused directly or used as a basis for further editing.

As a result, companies benefit from:

• Automatic reuse: Previously translated content does not need to be created again.
• Faster translation processes: Suitable translation suggestions are available immediately.
• Greater consistency: Recurring phrases and specialised terms are used consistently.

Not every match is identical – that is why Translation Memories differentiate between various match types according to their degree of similarity.

The more exact a match is, the less post-editing is generally required. The most important match types are:

Context Match (also known as a 101% match):
The sentence is identical and is also located in the same context, for example in the same chapter or document. This type of match offers the highest level of certainty.

100% Match:
The sentence matches an existing translation exactly and can generally be used directly.

Fuzzy Match:
The sentence is similar but contains minor deviations, such as changed numbers, additional words or different wording. The Translation Memory suggests the existing translation as a basis.

No Match:
There is no suitable translation for the sentence in the Translation Memory. It therefore has to be translated completely from scratch.

This enables companies to benefit from:

• Faster translation processes: The higher the match level, the lower the editing effort.
• More consistent translations: Previously approved wording is reused consistently.
• Lower translation costs: Recurrent content can be used efficiently.

Yes. A central translation memory ensures consistent corporate language across all departments.

Multiple employees, in-house translators and external language service providers can access the same translation memory simultaneously. As soon as a translation has been approved, it is immediately available to everyone involved for future projects.

This offers companies:

• Centralised management: All translation knowledge is managed in one central location.
• Maximum consistency: Consistent wording and terminology across all projects, departments and markets.
• No duplicated effort: Previously translated content can be reused by all teams.

A Translation Memory grows with every approved translation and develops into a valuable knowledge base for your company.

Modern translation systems can manage millions of text segments and quickly provide suitable translations even with very large data volumes. This ensures that all of your company’s translation knowledge is retained over the long term and can be reused at any time.

This enables:

• Long-term knowledge building: Already translated content is retained permanently and remains available even years later.
• High scalability: Even large Translation Memories containing millions of segments can be managed efficiently.
• Quick access: Existing translations are automatically found and provided as suggestions.

Yes – provided that the translation platform used complies with GDPR requirements.

It is not the translation memory itself that is decisive, but how and where the translation data it contains is stored, processed and protected. Companies should therefore ensure a data protection-compliant infrastructure and transparent data processing.

Important criteria include:

• GDPR-compliant server infrastructure: Storage and processing of translation data in compliance with applicable data protection requirements – ideally within the EU.
• Responsible handling of company data: Translation data must not be used for training external AI models or other purposes without consent.
• Secure role and permission concept: Only authorised persons have access to confidential company and translation data.

ownvia connects Translation Memories, terminology, style guides and AI in an intelligent translation process.

Existing translations from the Translation Memory are integrated directly into the AI translation process together with glossaries, style guides and reference documents. This produces translations that reliably reflect your terminology, writing style and existing corporate language.

Benefits for companies:

• Higher translation quality: The AI uses approved translations as a reliable contextual foundation.
• Consistent corporate language: Terminology and tone of voice remain consistent across all languages and channels.
• Less post-editing: Combining Translation Memory, terminology and AI significantly reduces the post-editing effort.
• More value from existing knowledge: Existing translations are actively incorporated into new AI translations, creating long-term value.

Translation Memories make translation processes faster, more cost-effective and more consistent in the long term.

With every completed translation project, the Translation Memory grows and develops into a valuable knowledge repository for your company. This digital asset can be automatically reused in future projects and improves both conventional translations and modern AI-assisted translation processes.

The key benefits at a glance:

• Lower translation costs: Previously translated content does not need to be created again.
• Shorter project timelines: Recurring content is immediately available as a translation suggestion.
• Consistent corporate language: Terminology and phrasing remain consistent across all markets and languages.
• Sustainable knowledge building: Your company’s translation knowledge is retained over the long term and can be reused at any time.

Terminology refers to the entirety of all specialist and company-specific terms, designations and abbreviations used within a company or industry. It clearly defines which terms are used for products, services, components or processes, thereby ensuring consistent communication.

Maintained terminology offers companies numerous benefits:

• Clarity: Every term has a clearly defined meaning and is used consistently.
• Brand consistency: Consistent terminology strengthens the corporate language and ensures a professional brand presence.
• Technical precision: Correct technical terms improve the quality of technical documentation, contracts, marketing texts and translations.
• Fewer errors: Consistent terminology reduces misunderstandings and avoids inconsistent wording.
• Better AI translations: Modern AI systems achieve significantly more precise results when they can draw on defined company terminology.

In short: Terminology forms the basis for consistent communication, high-quality translations and the successful use of AI within a company.

Further specialist article

AI-powered terminology extraction: Creating company glossaries efficiently

Find out how AI automatically identifies technical terms, builds glossaries and sustainably improves the quality of translations and AI-generated content.

→ Read the specialist article

Without centralised terminology management, different departments often use different terms for the same products, services or processes. This leads to misunderstandings, an inconsistent corporate language and unnecessarily high translation costs.

The key benefits at a glance:

• Consistent corporate language: Consistent terminology ensures a professional brand presence across all products, marketing channels and languages.
• Significant cost savings: The more consistent source texts and technical terms are, the faster and more cost-effectively translations can be produced.
• Legal certainty and precision: Clear terms in technical documentation, operating instructions or contracts reduce misunderstandings and minimise liability risks.
• Better AI translations: Predefined technical terms help AI systems use product names, specialist terms and company-specific wording correctly and consistently.

Further reading

Terminology extraction with AI: Efficiently creating company glossaries

Find out how AI automatically identifies technical terms, builds glossaries and sustainably improves translation quality.

→ Read the article

A terminology glossary is a structured collection of approved technical terms with their corresponding definitions, translations and usage rules. It serves as a central reference work and ensures that terms are used consistently throughout the company and in all languages.

A terminology glossary typically contains:

• Approved technical terms: Preferred terms for products, services, components or processes.
• Translations: Defined terms for all target and company languages.
• Definitions: Explanations that describe the unambiguous meaning of a term.
• Rules and restrictions: Do-not-translate terms, permitted abbreviations, unwanted synonyms or guidance on correct usage.

A well-maintained terminology glossary helps companies standardise translations, improve the quality of AI translations and maintain consistent corporate language across all departments.

Although both terms are often used synonymously, there is an important difference:

• Terminology describes the entire body of industry- and company-specific terms, as well as the rules governing their use.
• A glossary is the document or directory in which these terms, translations and additional information are collected and maintained.

In short: Terminology is a company's linguistic knowledge – a glossary documents this knowledge.

A terminology database (also called a termbank) is the central platform for managing company-wide terminology.

Unlike a simple glossary, which is often maintained as an Excel spreadsheet, a terminology database manages terms in multiple languages and in a structured database.

The key benefits include:

• Comprehensive information: Terms can be supplemented with definitions, contextual examples, parts of speech, approval status, images or sources.
• Shared use: Multiple departments, external translators or service providers can access the same terminology simultaneously.
• Direct system integration: Terminology databases can be connected to CAT tools, AI platforms, editorial systems and content management systems.

TBX (TermBase eXchange) is the international standard (ISO 30042) for exchanging terminology data.
The format was specifically developed to exchange glossaries and terminology databases between different software solutions without any loss of data.

Companies often use terminology in various systems – for example, in CAT tools, editorial systems, content management systems or AI platforms. The TBX format ensures that technical terms, translations, definitions and other terminology information are fully preserved and can be transferred to other systems without extensive post-processing.

The key advantages of TBX:

• Vendor independence: Terminology data can be exchanged between different software solutions.
• International standardisation: As an ISO standard (ISO 30042), TBX is supported by numerous translation and terminology systems.
• Long-term data security: Companies remain independent of individual software providers and can continue using their terminology over the long term.
• Efficient collaboration: Translation service providers, internal teams and external partners work with the same terminology and avoid inconsistent technical terms.

In short:
TBX is the standard format for the professional exchange of terminology data and ensures that corporate knowledge is preserved across systems.

Depending on the software used, company size and application scenario, different file formats are used:

• TBX (.tbx): The international ISO standard for the lossless exchange of professional terminology data between different systems.
• CSV (.csv): A simple, universal format for quickly importing, exporting and editing data in table form.
• Excel (.xlsx): Ideal for creating, maintaining and exchanging smaller glossaries.
• MultiTerm (.sdltb): Proprietary terminology format used by CAT tools such as Trados Studio.
• Translation memories (.tmx): Although TMX is not a terminology format, modern translation platforms can automatically extract technical terms from it and transfer them to glossaries or terminology databases.

Seamless reuse

The format used depends on your existing systems. Professional translation platforms enable the direct import of existing terminology data and help reuse existing corporate knowledge without having to rebuild it manually.

Modern AI translation systems can integrate glossaries and terminology databases directly into the translation process. Combined with translation memories and other corporate knowledge, approved technical terms, product names and company designations are consistently taken into account.

Without terminology, AI decides which translation fits best based on statistical probabilities. With a corporate glossary, it receives clear specifications and uses defined terms consistently across all documents.

The benefits at a glance:

• Consistent technical terminology in all languages
• Less post-editing for translators
• Consistent corporate language across all content
• Higher quality for technical, medical and legal texts

In short:
Only the combination of AI, terminology, translation memories and corporate knowledge enables professional translations that are technically accurate, consistent and aligned with the brand.

Further specialist article

AI integration into business processes: How can AI be successfully integrated?

Find out why AI only reaches its full potential when it is connected with corporate knowledge, terminology and existing processes.

→ To the specialist article

A company glossary is created by systematically collecting, evaluating and approving important specialist terms, product names and company-specific wording. The aim is to establish consistent corporate language and make this knowledge usable for translations, content creation and AI applications.

The most important steps in the process:

• Identify specialist terms: Collect relevant terms from websites, documentation, product materials or translation memories.
• Define preferred terms: Specify preferred terms and exclude unwanted synonyms or spellings.
• Define translations: Establish binding terms for all target and corporate languages.
• Add definitions and rules: Store explanations, do-not-translate requirements and usage rules.
• Maintain the glossary regularly: Add new terms and continuously update existing entries.

Today, this process no longer has to be carried out entirely manually. Modern AI solutions automatically identify specialist terms in existing documents, websites or translation memories and significantly speed up the creation of a company glossary.

In short:
A company glossary is created by systematically collecting and maintaining specialist terms. AI can automate this process much more quickly and provides the foundation for consistent communication and high-quality AI translations.

Creating a glossary manually is time-consuming and prone to errors. Modern AI systems can now automatically extract relevant technical terms from existing company data, significantly accelerating the creation of a company glossary.

Typical data sources for terminology extraction include:

• Company websites and landing pages
• Technical documentation and user manuals
• Existing translation memories
• Product catalogues as well as ERP and PIM data
• Existing Excel lists or incomplete glossaries

How AI supports this process:
The AI analyses large volumes of text, recognises recurring technical terms and company-specific formulations, identifies their meaning in the respective context and suggests suitable translations for additional target languages. The suggestions can then be reviewed by subject-matter experts and incorporated into a central company glossary.

In short:
AI automates the laborious search for relevant technical terms and helps companies build high-quality glossaries much faster and more efficiently.

Further specialist article

How does AI-powered terminology extraction work?

Find out how AI automatically recognises technical terms and uses them to create professional company glossaries.

→ Read the specialist article

Terminology should be maintained continuously and reviewed regularly. New products, software updates, amended brand guidelines or legal requirements mean that technical terms may need to be added, adapted or replaced.

Why regular updates are important:

• Up-to-date company knowledge: New technical terms are made available promptly to all employees, translators and AI systems.
• Avoiding outdated terms: Terms that are no longer approved or have become outdated are removed or marked as not permitted.
• Higher quality in AI translations: AI systems always access the latest terminology and deliver more consistent results.
• Consistent communication: Translations and content remain linguistically consistent even over many years and across numerous projects.

Proven approach:
Many companies update their terminology as needed, for example when launching new products, rebranding or releasing software. In addition, regular reviews are recommended – for example, once a quarter – to assess new terms, update existing entries and continuously develop the company glossary.

In short:
Terminology is not a one-off project, but an ongoing process. Regular maintenance ensures that company knowledge, translations and AI applications remain up to date over the long term.

Professional terminology management ensures that technical terms, product names and company-specific wording are used consistently throughout the organisation. This improves the quality of corporate communication and results in more consistent translations as well as better outcomes in AI-assisted translation processes.

The key benefits at a glance:

• Consistent corporate language: Strengthens a consistent corporate identity across all channels, products and target markets.
• Higher translation quality: Reduces misunderstandings and ensures the technically accurate use of terminology.
• Fewer corrections and less rework: Saves time during proofreading, approvals and post-editing.
• Lower translation costs: Reduces queries and increases the reuse of approved technical terms.
• Faster time to market: Multilingual content such as websites, product information and documentation can be published more quickly.
• Better results with AI translation: AI systems take approved terminology into account and generate more consistent translation suggestions.
• Higher customer satisfaction: Consistent and easy-to-understand content builds trust and reduces misunderstandings.
• Long-term preservation of corporate knowledge: Expertise remains centrally documented and is permanently available to employees and translators.

In short:
Professional terminology management provides the foundation for consistent communication, high-quality translations and the successful use of AI in the organisation.

ownvia integrates terminology directly into the AI-powered translation process. Existing company glossaries can be imported or created with the help of AI from websites, documents and translation memories. This makes company knowledge immediately available during translation.

During translation, ownvia takes approved specialised terms, product names and company-specific requirements into account. This results in more consistent translations and reduces the effort required for post-editing.

An overview of the key terminology features of ownvia:

• Import existing glossaries: Support for common formats such as Excel, CSV and TBX.
• AI-powered terminology extraction: Automatic identification of relevant specialised terms from websites, documents and translation memories.
• Multilingual terminology management: Central management of specialised terms, translations and usage rules.
• Integration into AI translations: Consideration of approved terminology throughout the entire translation process.
• Continuous expansion of company knowledge: Glossaries can be expanded and updated at any time.

In short:
ownvia combines AI-powered translation with terminology, translation memories and other company knowledge. This results in more consistent translations that better comply with company-specific language requirements.

Further reading

Terminology extraction with AI: The complete guide for businesses

Learn how companies can automatically extract specialised terms from websites, documentation and translation memories and use them to build a central company glossary.

→ Read the in-depth article

Companies use thousands of technical terms, product names and company-specific expressions every day. If these are not managed centrally, different terms for the same concept quickly arise. This leads to misunderstandings, inconsistent translations and an inconsistent corporate language.

With professional terminology management, approved terms are maintained centrally and made available to all employees, translators and AI systems. This ensures that everyone involved uses the same terminology – regardless of department, language or location.

Why terminology management is indispensable:

• Avoids inconsistent technical terms in documentation, translations and marketing materials.
• Improves collaboration between specialist departments, translators and external service providers.
• Creates a consistent corporate language across all products, markets and communication channels.
• Supports AI-powered translations through clear linguistic guidelines and approved terminology.
• Safeguards valuable corporate knowledge, ensuring that technical terms are retained even when employees or service providers change.

In short:
Terminology management prevents inconsistent communication and creates the basis for a consistent corporate language, high-quality translations and the successful use of AI.

An AI prompt is an instruction for an AI system. It specifies which task the AI should perform, which context it should take into account and what the desired result should look like.

The more precisely a prompt is formulated, the higher the quality and relevance of the result. Professional AI prompts therefore usually contain much more than just a simple question or instruction. In a business context, they are additionally supplemented with terminology, style guides, reference documents and other company knowledge in order to achieve consistent and reproducible results.

Typical components of a good AI prompt:

• Objective: What exactly should the AI do?
• Context: Which background information is important?
• Target audience: Who is the result intended for?
• Tone and style: Should the text be written in a professional, casual or factual style, for example?
• Format: Should the result be provided as a table, bullet points, an email or continuous prose?
• Subject-matter expertise: Which technical terms, company requirements or reference information should be taken into account?

An example:

❌ Simple prompt:

“Write a text about our product.”

✅ Professional prompt:

“Create a short product description for our website. The target audience is young adults. Use a casual tone, take our technical terminology into account and present the result in three bullet points.”

In short:
An AI prompt is the foundation of every AI request. The more clearly the objectives, context and requirements are described, the more precise and consistent the results will be.

The quality of an AI output depends largely on the quality of the prompt. If important information is missing or the instructions are unclear, the AI often produces generic, incomplete or technically inaccurate results.

A precise prompt gives the AI clear instructions and provides the necessary context. This results in outputs that are better suited to the respective task, target audience and company language.

The key benefits at a glance:

• Less post-processing: Precise prompts reduce the effort required for corrections and revisions.
• Higher productivity: Clear instructions lead to the desired results more quickly.
• Consistent company language: Technical terminology, tone of voice and brand communication are taken into account more consistently.
• Reproducible results: Standardised prompts deliver reliable results even for recurring tasks.

A practical example:

❌ Imprecise:

"Translate this text into English."

✅ Precise:

"Translate this text into English for medical technicians. Use a factual writing style, take our technical terminology into account and ensure precise language that complies with applicable standards."

In short:
Good AI prompts provide the AI with all the relevant information and create the basis for high-quality, consistent and reproducible results.

Prompt Engineering describes the systematic development, optimisation and management of AI prompts. The aim is to design AI requests in such a way that they consistently deliver high-quality and reproducible results.

While individual prompts are often created spontaneously, Prompt Engineering follows a structured approach. Prompts are developed, tested and continuously improved. In a business context, terminology, style guides, reference documents and other company knowledge are also incorporated so that the AI delivers results that meet the company's standards.

The key tasks of Prompt Engineering include:

• Development of professional AI prompts
• Optimisation of existing prompts
• Standardisation of recurring tasks
• Integration of company knowledge
• Consideration of terminology and style guides
• Continuous improvement of results

An example:

❌ Without Prompt Engineering:

A new prompt is created manually for each translation.

✅ With Prompt Engineering:

A company develops a standardised prompt template that automatically takes terminology, the style guide and company knowledge into account and is reused for all marketing texts.

In short:
Prompt Engineering ensures that AI systems consistently deliver high-quality and transparent results.

A professional AI prompt consists of much more than a simple instruction. It provides the AI with all the information it needs to complete a task as precisely and consistently as possible.

Depending on the use case, different types of information can be added. Particularly in a business context, terminology, style guides, reference documents and company knowledge play an important role alongside the actual task description.

Typical components of a professional AI prompt include:

• Task and objective
• Background information and context
• Target audience
• Tone and writing style
• Output format
• Technical terminology
• Company knowledge
• Reference documents
• Examples of the desired result

An example:

❌ Simple prompt:

“Translate this document.”

✅ Professional prompt:

“Translate the user manual into French. Use our terminology, follow the style guide, use objective language and return the result in the original format.”

In short:
A professional AI prompt combines a task description, context and company knowledge into a structured set of instructions. This results in high-quality, consistent and reproducible AI outputs.

Standard prompts often deliver good results for general tasks. However, they are often not sufficient in day-to-day business, as they contain no information about products, technical terms, brand guidelines or internal processes.

Without this context, AI can create linguistically correct content, but it does not take the company's individual requirements into account. This can lead to inconsistent communication, incorrect terminology or additional editing work.

That is why businesses often supplement their AI prompts with:

• Company knowledge
• Technical terminology
• Style guides
• Reference documents
• Target audience information
• Guidelines for the company's language

One example:

❌ Standard prompt:

“Create a newsletter about our new product.”

✅ Professional business prompt:

“Create a newsletter for existing customers. Use our corporate language, take the terminology from our glossary into account and write the text in the style of our previous marketing campaigns.”

In short:
Standard prompts deliver general results. It is only through company knowledge that content is created which is technically accurate, consistent and aligned with the brand.

Company knowledge provides AI with the subject-matter and organisational context it needs to produce high-quality results. This includes, for example, product information, internal guidelines, specialist terms, target audiences or already approved content.

The more relevant knowledge is incorporated into an AI prompt, the better AI can understand the requirements and generate suitable results.

Company knowledge includes, among other things:

• Product and service descriptions
• Specialist terminology
• Style guides
• Company policies
• Reference documents
• Approved translations and content

An example:

❌ Without company knowledge:

AI creates a general product description.

✅ With company knowledge:

AI uses the correct terminology, takes the desired tone of voice into account and aligns with previously published company content.

In short:
Company knowledge adds the crucial context to AI prompts, thereby improving the quality, consistency and relevance of the results.

Terminology and style guides provide the AI with clear linguistic guidelines. While terminology specifies which technical terms should be used, the style guide defines, among other things, tone, form of address and writing style.

This information can be integrated directly into the AI prompt or provided through stored company guidelines, glossaries and reference documents. This creates content that meets the company’s linguistic and technical standards.

Typical guidelines in a professional AI prompt include:

• Technical terminology: Which terms should be used or avoided.
• Brand and product names: Consistent spelling for products, features and company names.
• Tone and form of address: For example, formal or informal address, factual, professional or casual.
• Language rules: Guidelines for sentence structure, style or phrasing.
• Formatting: Information on headings, tables, lists or paragraph structure.

An example:

❌ Without guidelines:

“Write a blog post about our product.”

The AI uses different technical terms, switches between formal and informal address and does not align with the company’s language.

✅ With terminology and style guide:

“Write a blog post about our product. Consistently use formal address, use only the approved technical terms from our glossary and follow our factual, professional writing style.”

The result takes the company’s language into account, uses the desired terminology and ensures consistent communication.

In short:
Terminology and style guides help the AI create content that is technically accurate, linguistically consistent and aligned with the company’s communications.

Reference documents provide the AI with concrete examples of content, structure, and language style. Instead of merely describing to the AI what the result should look like, they use previously approved documents to show which wording, structure, and level of detail are required.

This helps the AI understand the desired context better and create new content based on existing company documents.

Suitable reference documents include, for example:

• Already published content: Blog posts, newsletters, or customer communications.
• Technical documentation: User manuals, data sheets, or product specifications.
• Marketing and sales materials: Brochures, presentations, or case studies.
• Company documents: Policies, process descriptions, or training materials.

An example:

❌ Without a reference document:

“Create a product description for our new module.”

The AI independently chooses the structure, writing style, and level of detail.

✅ With a reference document:

“Create a product description for our new module. Use the attached product description of our existing module as a guide for the structure, level of detail, and language style.”

The new product description follows the style of the existing documentation and integrates seamlessly into the existing product range.

In short:
Reference documents provide the AI with a concrete model and help create content that matches existing company documents in terms of subject matter, language, and structure.

Many AI requests in companies are repeated every day – e.g. for translations, summaries or marketing copy. Instead of every team member reinventing the wheel, tried-and-tested prompts are made available centrally as standardised templates (prompt templates).

Such templates often work with flexible placeholders (such as [product name] or [target audience]), while fixed specifications for the style guide, tone of voice and company knowledge are already integrated. They are usually made available via internal knowledge bases, prompt management tools or as custom company GPTs.

The key benefits of standardised prompt templates:

• Consistent quality: All employees use approved best practices instead of vague individual creations.

• Maximum time savings: No need to spend a long time thinking about the right wording – copy the template, fill in the placeholders, and you’re done.

• Efficient onboarding: New team members achieve professional AI results from day one.

• Centralised maintenance: If the brand identity changes, only the central prompt template needs to be updated.

An example:

❌ Without standardisation:

“Write me a summary of this PDF.”

Each person formulates the prompt differently and gets results with different structures, lengths and levels of detail.

✅ With a prompt template:

“Summarise the following document. Structure the result as follows: 1. Key message (maximum two sentences), 2. The three most important insights as bullet points, and 3. Specific recommendations for action for the team.”

The summaries have a consistent structure and are easier to compare and process further.

In short:
Standardised prompt templates ensure consistent AI results, accelerate workflows and help companies safeguard their quality standards over the long term.

As AI usage increases, companies often create numerous prompts for different tasks. Without centralised management, employees use different versions, which can result in a loss of quality, consistency and traceability.

That is why many companies rely on centralised management of their AI prompts. Proven prompt templates are stored, maintained and made available to all employees in one central location. As a result, changes to terminology, style guides or company policies only need to be made once and are then available throughout the organisation.

Prompt templates are managed, for example, in:

• Centralised AI platforms
• Knowledge bases
• Documentation systems
• Specialised prompt management solutions

The key benefits of centralised prompt management:

• Consistent prompt templates: All employees access the same approved templates.
• Centralised maintenance: Changes only need to be made once and are immediately available to everyone.
• Consistent quality standards: Company requirements are taken into account consistently.
• Better collaboration: Proven prompts can be easily shared and further developed.

An example:

❌ Without centralised management:

"Create a quotation for our customer."

Each department uses different prompts, product names and wording.

✅ With centralised prompt management:

All employees use the same approved prompt template for quotation letters.

The quotations follow a consistent structure, use up-to-date product information and comply with the defined company standards.

In short:
Centralised prompt management helps companies provide proven AI prompts consistently, maintain them continuously and reuse them throughout the organisation.

AI prompts are not static templates; they evolve alongside a company’s requirements. New products, changed company policies or experience from daily use make regular adjustments worthwhile.

Through continuous optimisation, AI prompts become more precise and deliver better results over the long term. At the same time, companies ensure that terminology, style guides and company knowledge are always taken into account and kept up to date.

AI prompts can be improved, for example, through:

• Feedback from everyday work: Employees’ experiences help to further develop prompts in a targeted way.
• New products or services: Changes to the portfolio are incorporated into existing prompt templates.
• Updated terminology and style guides: New technical terms or changed company requirements are incorporated into the prompts.
• Up-to-date company knowledge: New reference documents, policies or content improve the professional context.
• Further development of AI models: New models or features may make adjustments to existing prompts advisable.

An example:

❌ Outdated prompt template:

"Create a product description for version 2.0. Use the 2023 glossary."

The AI uses outdated product information and does not take current company requirements into account.

✅ Updated prompt template:

"Create a product description for version 3.0. Use the current terminology, take the revised style guide into account and refer to the latest product information."

The generated text complies with current company standards and uses the latest information.

In short:
Regularly maintained AI prompts ensure that results remain up to date, consistent and aligned with the company’s requirements.

When employees work with AI without clear guidelines, typical mistakes can quickly creep in. The result is inaccurate output, additional correction work and inconsistent corporate communications.

Most problems are not caused by the AI itself, but by incomplete, contradictory or overly general instructions.

An overview of the most common mistakes:

• Overly general wording: If the target audience, tone or format is missing, the AI often produces generic or poorly suited results.
• Too many tasks in a single prompt: When several complex tasks are requested at the same time, the quality of the results often suffers.
• Lack of context: Without company knowledge, reference documents or examples, the AI lacks important information.
• No clear guidelines: If the wording to use or avoid is not defined, inconsistent results are produced.
• Different prompt versions: If each team uses its own prompts, consistency and quality standards are lost.

An example:

❌ Unstructured prompt:

"Write a long text about our product and also create five social media posts and an email to go with it."

The AI has to solve several tasks at the same time and often produces superficial results.

✅ Structured prompt:

"Create a product description for our website. The target audience is medium-sized industrial companies. Take our terminology and style guide into account and limit the text to a maximum of 200 words."

The AI receives clear guidelines and produces a result that fits the requirements much better.

In short:
Most prompt engineering mistakes are caused by a lack of context, unclear instructions and insufficient standardisation. Clear structures and consistent prompt templates help to avoid these mistakes.

Prompt templates are predefined instructions for recurring tasks. They already contain important specifications such as the objective, tone, format, terminology or company knowledge. Employees only need to add the variable information, such as a text, product name or customer data.

This means that tried-and-tested AI prompts do not have to be created from scratch every time. At the same time, prompt templates ensure consistent results and simplify the use of AI throughout the company.

The key advantages at a glance:

• Save time: Recurring tasks can be completed more quickly because tried-and-tested prompt templates can be used directly.
• Consistent quality standards: All employees work with the same approved specifications.
• Less reworking: Terminology, style guides and company knowledge are taken into account from the outset.
• Faster onboarding: New employees can use existing prompt templates and familiarise themselves with recurring workflows more quickly.
• Easy updating: Changes to company policies or terminology only need to be made once in the template.

An example:

❌ Without a prompt template:

“Translate this email into English.”

The tone may vary and technical terms may be translated inconsistently.

✅ With a prompt template:

“Translate the following text into English. Use our glossary for technical terms, maintain a formal tone and follow our factual yet friendly B2B writing style.”

The translation takes terminology, tone and company specifications into account from the very first draft.

In short:
Prompt templates standardise recurring AI tasks, save time and help businesses achieve consistent, high-quality results.

Professional AI prompts do not have to be created entirely manually. Modern AI systems can help analyse existing information and develop structured prompt templates from it. This approach is also often referred to as Meta-prompting.

For example, company websites, style guides, reference documents or other sources of knowledge can be evaluated. AI identifies recurring rules, tones of voice and requirements and transfers them into a structured prompt template that can then be individually adapted and further developed.

AI supports the creation of professional prompts in several ways, including:

• Analysis of existing content: Style guides, reference documents or company information are evaluated.
• Structuring requirements: Notes or briefs are turned into clearly structured prompt templates.
• Optimisation of existing prompts: Existing prompts are analysed and improved in a targeted manner.
• Integration of company knowledge: Terminology, tone of voice and other requirements can be taken into account directly.
• Creation of test cases: Example content helps to review and further develop new prompt templates.

An example:

❌ Manual creation:

An employee manually transfers the most important rules from an extensive style guide into a new prompt.

This is time-consuming, and important requirements can easily be overlooked.

✅ AI-supported creation:

"Analyse the attached style guide and create a prompt template for our marketing copy. Take tone of voice, terminology, no-gos and placeholders for product information into account."

AI creates a structured draft that can then be reviewed and refined further if necessary.

In short:
AI helps companies create professional prompt templates more quickly and systematically transfer company knowledge into reusable AI instructions.

ownvia does not view AI prompts as individual text inputs, but as part of a structured translation process. The goal is to make company knowledge permanently usable for AI translations and to standardise translation processes.

To achieve this, ownvia combines AI prompts with terminology, style guides, reference documents, translation memories and other company knowledge. Instead of creating a new prompt for every translation, companies develop reusable prompt templates that can be managed centrally, continuously maintained and used throughout the company.

The integrated AI prompt assistant helps create professional translation prompt templates. For example, company websites, style guides, reference documents or existing translations can be analysed for this purpose. Based on this information, ownvia develops structured prompt templates that can then be individually customised and continuously refined.

A particular advantage is that these prompt templates are not static. For example, if companies receive feedback from, e.g., post-editors, the relevant requirements can be incorporated directly into the template. This allows the prompt template to improve continuously, while future translations automatically take this knowledge into account.

With ownvia, companies can, among other things:

• Create and manage translation prompt templates centrally
• Reuse prompt templates throughout the company
• Integrate terminology, style guides and reference documents
• Incorporate translation memories and company knowledge into AI translations
• Develop and optimise translation prompt templates using the AI prompt assistant
• Continuously refine prompt templates based on new insights and feedback

An example:

❌ Without ownvia:

“Translate this technical manual into English.”

Each employee creates their own prompts. The results differ in terms of terminology, tone and quality. Insights from previous translation projects are often lost.

✅ With ownvia:

The translation team uses a central prompt template for technical documentation. It already contains terminology, the style guide, reference documents and other company requirements. Following feedback, the prompt template is adjusted so that future translations automatically take this requirement into account.

All employees use the same approved prompt template. New insights are continuously incorporated, improving the quality of future translations.

In short:
ownvia combines AI prompts with company knowledge, terminology and translation memories to create reusable prompt templates. These can be continuously refined and ensure that experience from every translation project remains permanently available for future AI translations.

Organisational knowledge encompasses all the information, data and experience that a company builds up over time and uses for its day-to-day business processes. It forms the basis for informed decisions, efficient workflows and consistent communication.

This includes both documented information and the knowledge and experience of employees. Together, they ensure that processes run efficiently, decisions are based on reliable knowledge and a company communicates consistently both internally and externally.

Organisational knowledge includes, among other things:

• Corporate communications: Brand identity, writing style, tone of voice and communication guidelines
• Expertise: Industry-specific know-how, technical terms and company standards
• Product and service knowledge: Information about products, services and solutions
• Company content: Approved information, documentation and published content
• Process knowledge: Policies, workflows and proven approaches
• Experiential knowledge: Insights from projects and continuously accumulated know-how

A practical example:

A company creates quotes, marketing materials and technical documentation. If everyone involved draws on the same knowledge base, language, technical terms and content remain consistent across all areas. This reduces the need for coordination and ensures a consistent corporate presence.

✅ In short:
Organisational knowledge encompasses the information and experience that distinguish a company. It forms the basis for efficient processes, informed decisions and consistent communication.

Modern AI systems have extensive general knowledge and can write, translate or summarise texts. However, they do not automatically know company-specific information such as technical terms, internal guidelines or the company’s preferred language.

Only when relevant company knowledge is incorporated into AI processes can AI create content that is aligned with a company’s specific requirements, quality standards and communication. This results in more consistent outcomes and reduces the effort required for subsequent corrections.

Company knowledge improves, among other things:

• Technical accuracy: Content is aligned with the company’s products, services and requirements.
• Consistent communication: Language, terminology and phrasing remain consistent across different types of content.
• Higher quality: Content better meets the defined quality standards.
• More efficient workflows: Less manual post-processing saves time and increases productivity.
• Stronger brand communication: Consistent content ensures a professional and recognisable company presence.

A practical example:

A company creates product information for its website, sales materials and technical documentation. Without a shared knowledge base, different wording, technical terms and messages often arise. This makes the company’s communication appear inconsistent.

If the same knowledge base is taken into account when creating all content, the language, terminology and company communication remain consistent – regardless of where the content is published.

✅ In short:
Company knowledge provides AI with the necessary context to create content that is not only linguistically correct, but also technically precise, consistent and aligned with a company’s requirements and communication.

Company knowledge encompasses far more than individual documents. It consists of different types of information that a company builds up over time and uses in its day-to-day work. Together, they form the basis for well-informed decisions, consistent communication and efficient work processes.

The more complete and up to date this knowledge is, the easier it is for employees and AI applications to access a shared knowledge base and achieve high-quality results.

Company knowledge includes, for example:

• Company communication: Language, brand identity and communication guidelines

• Expert knowledge: Industry-specific expertise and proven practices

• Product and service knowledge: Information about products, services and solutions

• Company processes: Internal procedures, guidelines and quality standards

• Experiential knowledge: Insights from projects and continuously developed expertise

• Legal and regulatory requirements: Statutory requirements and internal company guidelines

A practical example:

A company creates content for marketing, sales and customer service. If everyone involved draws on the same knowledge base, language, statements and specialist content remain consistent across all areas.

✅ In short:
Company knowledge consists of different types of information that together support a consistent way of working, consistent communication and well-informed decisions within the company.

Modern AI systems have extensive general knowledge and can support a wide range of tasks. However, they do not have automatic access to internal company information. This includes, for example, individual processes, product details, internal guidelines or company communications.

At the same time, this protects sensitive company data. Each company decides for itself which information is made available to AI. Only when relevant company knowledge is deliberately incorporated can AI take it into account when creating content or translations.

Company knowledge is not automatically taken into account because:

• Company data is confidential: Internal information is not automatically available to public AI systems.
• Every company works differently: Products, processes and working methods vary from one company to another.
• Company communications are unique: Technical terms, wording and brand messages are specific to each company.
• Quality standards vary: Each company defines its own requirements for content and documents.
• Data protection plays an important role: Companies decide for themselves which information they provide for AI applications.

A practical example:

Two companies are developing comparable software products. Although both use the same AI, their technical terms, processes, quality guidelines and company communications differ significantly. Without this company-specific information, AI can only create general content.

When the relevant company information is provided to AI, the results are aligned with the individual requirements, terminology and company communications of the respective company.

✅ In short:
AI does not automatically know your company’s knowledge. Each company decides which information is taken into account. Only by deliberately incorporating relevant company information can content be created that matches the company’s language, processes and quality standards.

Corporate knowledge becomes particularly valuable for AI when it is structured, up to date and aligned with the respective use case. Many companies already have a large amount of knowledge – for example in documents, guidelines or internal information sources. However, what matters is that this knowledge is maintained consistently and can be used for the respective task.

What matters is not the amount of information available, but its quality. Up-to-date, clear and reliable content enables AI systems to produce better results and align themselves with a company’s requirements.

The following points are particularly important for the successful use of corporate knowledge:

• Relevance: Only information that is actually needed for the respective task should be taken into account.
• Currency: Corporate knowledge should be reviewed regularly and kept up to date.
• Quality: Verified and consistent information leads to more reliable results.
• Structure: Clearly organised content makes it easier to use and reduces misunderstandings.
• Availability: Relevant corporate knowledge should be available wherever it is needed in day-to-day work.

Typical challenges

Many companies already have extensive corporate knowledge. In practice, however, it is often:

• distributed across various departments or systems,
• no longer up to date,
• maintained inconsistently or
• difficult to find.

As a result, employees and AI applications often work with different information bases. A centralised and well-maintained knowledge base provides the foundation for consistent content and efficient work processes.

✅In short:
The quality of AI results is determined not by the amount of information, but by its relevance, currency and structure. Well-maintained corporate knowledge forms the basis for consistent, reliable and high-quality results.

Companies often already have a wide range of valuable documents that are suitable as a basis for using AI. They contain specialist knowledge, describe products and services, or define internal standards and processes. Often, this knowledge does not need to be created from scratch, but merely used systematically.

Documents that are regularly maintained, professionally reviewed and used as a reliable source of information in day-to-day business are particularly valuable. They help create content consistently and sustainably improve the quality of AI results.

Examples of particularly suitable documents include:

• Product and service descriptions: Provide knowledge about products, functions and areas of application.
• Technical documentation: Contains detailed information about processes, procedures and technical interrelationships.
• Marketing and sales materials: Describe the company’s language, target groups and brand communication.
• Corporate policies: Define internal requirements, quality standards and working methods.
• Training and knowledge documents: Document experience, best practices and internal know-how.

What should companies pay attention to?

Not every document is equally suitable as a source of knowledge. Outdated, contradictory or incomplete content can impair the quality of the results. It is therefore advisable to review documents regularly and use only approved and up-to-date information as a basis.

✅ In short:
Well-maintained corporate documents are among the most important sources of knowledge for AI. The more up-to-date and reliable the content, the better it can contribute to creating consistent, high-quality results.

Company websites are among a company’s most important sources of knowledge. They contain up-to-date information about products, services, technical terms, areas of application and corporate communications. As websites are regularly maintained, they often reflect a company’s current state of knowledge.

Unlike many internal documents, company websites are easily accessible and regularly updated. They are therefore particularly well suited to capturing information quickly and creating a consistent foundation for content, documentation or translations.

Why are company websites particularly valuable as a source of knowledge?

• Up-to-date information: New products, services and company developments are often published on the company website first.
• Consistent corporate communications: Language, tone of voice and brand messages are already aligned and reflect the desired corporate image.
• Technical expertise: Product pages, blog posts and FAQs convey industry-specific expertise and explain complex topics in an understandable way.
• High level of currency: Company websites are regularly maintained and often reflect the current status of products, services and company information.

A practical example:

A company publishes product descriptions, application examples and technical articles on its company website. These contents show not only which services are offered, but also which technical terms are used and how the company communicates with customers. This creates a reliable knowledge base that supports consistent communication across different areas of the company.

Good to know:

A company website generally represents only the publicly available part of a company’s knowledge. Internal expertise, work instructions and quality guidelines are often contained in additional documents and complement the publicly available information on the company website.

✅ In short:
Company websites are among a company’s most important sources of knowledge. They provide up-to-date information, convey the company’s communications and create a reliable foundation for consistent content.

Reference documents are already approved, high-quality company documents. They serve as a guide for new content and show how a company uses specialist information, terminology and corporate communications in practice.

As reference documents have already been reviewed and are used in the day-to-day running of the company, they are often among a company's most reliable sources of knowledge. They help create new content consistently and maintain existing quality standards.

Which reference documents are particularly suitable?

• Product brochures: Describe products, services and their benefits.
• Technical documentation: Convey specialist knowledge, processes and technical relationships.
• User manuals: Demonstrate a consistent structure as well as clear and precise wording.
• Marketing and sales materials: Illustrate corporate communications and brand messages.
• Training materials: Contain established knowledge, internal standards and best practices.

A practical example:

A company regularly creates new product information. Instead of reformulating each description from scratch, already published brochures and technical documentation serve as references. This ensures that the language, structure and specialist content remain consistent, even in new documents.

Good to know:

Reference documents should be reviewed regularly. Outdated or no longer valid content is only suitable to a limited extent as a knowledge base and should be replaced with up-to-date documents.

✅ In short:
Reference documents are among a company's most valuable sources of knowledge. They help create new content consistently, clearly and to a high standard, based on information that has already been reviewed.

Style guides are among the most important components of a company’s knowledge. They define how a company communicates and ensure that content is created consistently, regardless of the author, department or language.

They define binding rules for writing style, tone of voice and phrasing. This results in content that aligns with the brand, appears professional and remains consistent across all communication channels.

For example, a style guide can define:

• Form of address: Should the target audience be addressed formally or informally?
• Tone of voice: Should communication be factual, friendly or technical?
• Spelling and usage: How should product names, abbreviations or technical terms be used?
• Language rules: Are there guidelines for inclusive language, number formats or dates?
• Brand communication: Which phrases or terms should be preferred or avoided?

A practical example:

A company publishes content for its website, sales team and customer service. Without binding rules, the form of address, writing style and phrasing often differ between the individual areas. A style guide ensures that all content follows the same linguistic and communication standards.

Good to know:

A style guide does not replace terminology. While terminology defines which technical terms are used, a style guide describes how content is formulated and communicated. The two complement each other and together contribute to consistent corporate communication.

✅ In short:
Style guides ensure consistent corporate communication. They define binding language and writing rules and help create content that is consistent, professional and aligned with the brand.

Terminology comprises the defined technical terms used by a company and is one of the most important components of corporate knowledge. It ensures that products, services, processes and technical matters are referred to consistently everywhere.

Consistent terminology improves comprehensibility and reduces misunderstandings. At the same time, it helps employees, service providers and AI applications use the same terms consistently and ensure consistent corporate communication.

Consistent terminology creates a shared linguistic foundation and ensures that information is understood and used consistently throughout the company.

Why is terminology so important?

• Clear technical terms: Products, services and processes are referred to in the same way everywhere.
• Consistent communication: All areas of the company use the same language and avoid different terms for the same matter.
• Higher quality: Consistent terms improve comprehensibility and make it easier to review content from a technical perspective.
• More efficient collaboration: Employees, partners and service providers use the same terminology.
• Better results with AI: Consistent technical terms help create content that is consistent and technically precise.

A practical example:

A company refers to an application internally as the “customer portal”. However, its marketing materials call it the “service portal”, its documentation refers to it as the “user portal” and its support team calls it the “online portal”. Although the same thing is meant, this creates misunderstandings and inconsistent communication.

A defined terminology ensures that the same technical term is used in all areas. This improves comprehensibility and strengthens the consistency of corporate communication.

Good to know:

Terminology defines which technical terms are used. A style guide, on the other hand, describes how content is formulated – for example, in terms of tone, form of address or writing style. Both complement each other and together form a central foundation of corporate knowledge.

✅ In short:
Terminology is one of the most important components of corporate knowledge. Consistent technical terms improve comprehensibility, increase quality and ensure consistent communication – both between people and when using AI.

Company knowledge is constantly changing. New products, services, processes or legal requirements mean that existing information must be regularly supplemented or updated. Only up-to-date company knowledge can serve as a reliable basis for decisions and high-quality content in the long term.

Systematic knowledge management helps keep information up to date and provides everyone involved with a shared knowledge base. This keeps content consistent and ensures the quality of corporate communications over the long term.

How does company knowledge stay up to date?

• Review documents regularly: Specialist content should be checked and updated at defined intervals.
• Define responsibilities: It should be clearly defined who maintains and approves content for important areas of knowledge.
• Document changes centrally: New products, processes or guidelines should be incorporated into the existing knowledge base promptly.
• Remove outdated information: Content that is no longer valid should be archived or replaced.
• Continuously expand company knowledge: Experiences from projects, customer feedback or new requirements supplement the existing knowledge base.

A practical example:

A company launches a new product and updates its product descriptions, training materials and internal guidelines at the same time. This ensures that all employees have access to the same up-to-date information and communicate consistently – regardless of their department or area of responsibility.

Good to know:

Company knowledge is not a one-off project. It evolves with every new product, every process change and every new insight. Regular maintenance ensures that information remains reliable and useful over the long term.

✅ In short:
Company knowledge should be maintained and updated continuously. Only current, approved and reliable information creates a shared knowledge base and supports consistent decisions and high-quality content.

Centralized company knowledge ensures that information is consistently available throughout the company. Instead of distributing knowledge across different documents, departments or systems, everyone involved can access the same reliable knowledge base. This improves collaboration, reduces misunderstandings and makes well-founded decisions easier.

At the same time, centralized company knowledge provides the basis for consistent content, efficient processes and the successful use of modern AI applications. The more up-to-date and structured the knowledge base is, the easier it is to find information and use it across the company.

What are the benefits of centralized company knowledge?

• Consistent communication: All areas of the company use the same information, terminology and communication guidelines.
• Higher efficiency: Information does not need to be researched or created multiple times, which significantly reduces time and effort.
• Better collaboration: Employees, teams and external partners work on a shared, reliable knowledge base.
• Higher quality: Consistent information reduces errors and improves the quality of documents, content and decisions.
• Faster onboarding: New employees find important information more quickly and can become productive sooner.
• An optimal foundation for AI: Up-to-date company knowledge helps support AI applications with relevant information and achieve consistent results.

An example from practice:

A company manages product information, internal policies and communication guidelines centrally. Marketing, sales, customer service and technical writing all use the same information. This creates consistent content, reduces the need for coordination and ensures that new information is made available to all areas promptly.

Good to know:

Centralized company knowledge does not mean that all information has to be stored in a single system. What matters is that relevant information is up to date, unambiguous and easy for authorized people to find.

✅ In short:
Centralized company knowledge improves collaboration, increases the quality of information and creates a shared foundation for efficient processes, consistent corporate communication and the successful use of AI.

Company knowledge is used in almost every area of a company. It ensures that information is used consistently, decisions are made based on sound information and content is created efficiently. A central knowledge base supports both employees and modern AI applications in accessing up-to-date and reliable information.

The better company knowledge is structured and maintained, the more versatilely it can be used – from internal collaboration to communication with customers and business partners.

Typical use cases for company knowledge include:

• Marketing: Creating consistent websites, blog posts, brochures and campaigns.
• Sales: Consistent product information, quotations and presentations.
• Customer service: Faster and more consistent responses to customer enquiries.
• Technical documentation: Consistent user guides, manuals and technical documents.
• Translations: Consistent use of terminology, corporate language and specialist knowledge in all target languages.
• Product management: Consistent product descriptions and documentation throughout the entire product lifecycle.
• Human resources: Creating job advertisements, onboarding materials and internal policies.
• AI applications: Creating, analysing, summarising and translating content based on up-to-date and relevant company information.

A practical example:

A company is developing a new product. Marketing creates the product page, sales prepares sales materials, technical writers produce the documentation and customer service prepares answers to frequently asked questions. If all departments access the same knowledge base, consistent information is created throughout the entire product lifecycle.

Good to know:

Company knowledge delivers the greatest benefit when all relevant areas of the company have access to the same up-to-date information. This reduces duplicate work, simplifies coordination and sustainably improves the quality of information and decisions.

✅ In brief:
Company knowledge supports almost every area of a company – from marketing and sales to customer service and technical documentation, as well as translations and AI applications. A central knowledge base ensures consistent information, more efficient processes and higher quality.

Artificial intelligence can support corporate knowledge by helping to find information more quickly, create content efficiently and automate recurring tasks. However, this requires the AI to be able to access up-to-date, relevant and structured corporate information.

The better corporate knowledge is maintained and organised, the more precise and consistent the results an AI can deliver. Instead of providing general answers, it is guided by the company's defined information, terminology and communication guidelines.

How does AI support the use of corporate knowledge?

• Faster access to information: Relevant corporate knowledge can be found within seconds and used directly.
• More efficient content creation: AI supports the creation of texts, documentation, emails, presentations or knowledge articles based on existing corporate information.
• Consistent communication: Corporate language, terminology and style guidelines can be taken into account when creating new content.
• Support with translations: AI can use existing corporate knowledge to create consistent, high-quality translations.
• Knowledge management: Information from different sources can be accessed more quickly and made usable for employees.
• Reducing the burden of routine tasks: Recurring tasks such as summaries, documentation or answering frequently asked questions can be supported efficiently.

A practical example:

An employee creates a product description for a new market. The AI accesses approved product information, terminology and the company's communication guidelines and creates a precise first draft. This significantly reduces processing time and supports consistent corporate communication.

Good to know:

The quality of AI results depends to a large extent on the quality of the corporate knowledge provided. Outdated, incomplete or contradictory information directly affects the quality of the results. Regularly maintained corporate knowledge is therefore an important prerequisite for reliable and consistent AI outputs.

✅ In brief:
AI makes corporate knowledge usable more quickly and supports companies in creating, analysing and using information. The best results are achieved when up-to-date, structured and well-maintained corporate knowledge serves as the basis.

Company knowledge is the foundation that enables artificial intelligence to create company-specific content consistently, accurately and in the desired communication style. ownvia connects various knowledge sources with artificial intelligence so that content, documents and translations can be created based on up-to-date, relevant and company-specific information.

Instead of relying exclusively on the general knowledge of AI models, ownvia enables the targeted integration of company knowledge into the workflow. This allows specialist terms, terminology, corporate language and existing company knowledge to be specifically taken into account when creating new content. This improves consistency, reduces manual effort and supports consistent corporate communication across all departments and languages.

The more relevant and up-to-date company knowledge is available to an AI, the better it can take corporate language, terminology and existing content into account when handling new tasks. This results in more consistent outputs and can significantly reduce the effort required for corrections and post-editing.

Which knowledge sources can ownvia use?

• Company websites: Public information about products, services, processes and corporate communication.
• Reference documents: Manuals, technical documentation, guidelines, product information and other approved company documents.
• Terminology: Consistent specialist terms, product names and defined translations for consistent communication.
• Style guides: Specifications for tone, writing style, corporate language and linguistic guidelines.
• Translation memories: Previously translated content can be used to support consistency and reuse.
• Additional company sources: Process descriptions, training materials, quality guidelines or other approved internal documents can, for example, be integrated as part of the central knowledge base.

A practical example:

A company publishes product information on its website, prepares sales materials and translates technical documentation for international markets at the same time. ownvia uses terminology, translation memories, style guides, reference documents and other company information as a shared knowledge base. This creates consistent content across all languages, documents and communication channels, while significantly reducing the effort required for post-editing.

Good to know:

Company knowledge is not a static data set but continuously evolves with new products, processes, guidelines and experience. The knowledge base should therefore be regularly maintained and updated. The company itself decides which information is used as company knowledge. This keeps company knowledge up to date in the long term and provides a reliable foundation for AI applications, translations and consistent corporate communication.

✅ In short:
ownvia connects company knowledge with artificial intelligence so that companies can create content, translations and documents based on a central knowledge base. By combining company websites, reference documents, terminology, style guides, translation memories and other company sources, consistent results are produced that support corporate communication and make the use of AI within the company more efficient.

Translation quality describes how accurately, completely and consistently a translation conveys the content, meaning and intended effect of a source text in the target language. In addition to linguistic accuracy, high translation quality takes terminology, subject-matter context, style, target audience and company-specific requirements into account.

In professional translations, quality does not arise by chance. It is based on the interaction of AI systems or qualified translators, consistent terminology, company knowledge, suitable quality checks and – depending on the area of application – a final subject-matter review.

The most important characteristics of high translation quality

• Linguistic and subject-matter accuracy: Content is transferred accurately, completely and without errors.
• Consistent terminology: Technical terms and company language are used consistently in all translations.
• Completeness: All information is transferred without omissions or loss of meaning.
• Target-audience-appropriate style: The tone, writing style and wording correspond to the expectations of the target audience.
• Traceable quality assurance: Quality checks and AI-supported correction suggestions help to continuously improve translation quality. 

✅Example from practice

An AI translation may appear to be grammatically flawless. However, if technical terms are used inconsistently or safety-relevant warnings in an instruction manual are translated incorrectly, the translation does not meet the requirements of professional translation quality.

✅Good to know

Today, the quality of a translation is no longer measured solely by the end result. Consistent terminology, AI-supported quality checks, traceable correction suggestions and transparent review processes also make a decisive contribution to improving translations efficiently, safely and reliably.

Translation quality is influenced by a wide range of technical, linguistic and organisational factors. In addition to the quality of the source text, terminology, company knowledge, contextual information, the AI model used and suitable quality assurance measures play a particularly important role. Only the interaction of these factors enables consistent, technically accurate and target-group-specific translations.

Even high-performance AI systems can only deliver results that are as good as the information provided allows. Unclear wording, ambiguous technical terms or missing specifications regarding terminology, style and target audience increase the risk of mistranslations and unnecessary post-editing. Conversely, a structured data basis significantly improves quality as early as the first translation draft.

The most important influencing factors

• Source text quality: Clear, unambiguous and well-structured source texts form the basis for precise and comprehensible translations.
• Terminology and company knowledge: Glossaries, style guides, reference documents and translation memories help ensure that technical terms, brand language and company-specific wording are used consistently.
• Contextual information: Information about the target audience, document type, intended use or regulatory requirements enables a more precise interpretation of the source text.
• AI model and translation technology: Depending on the language model, subject area and language combination, the quality, style and consistency of the translation may vary.
• Quality assurance: AI-supported quality checks, as well as expert review by translators or subject matter experts, help to identify and correct linguistic, terminological and content-related errors at an early stage. 

✅Example from practice

An engineering company translates a technical operating manual into several languages. By using a well-maintained terminology glossary, a style guide and existing translation memories, the AI recognises the technical context and uses all safety-relevant terms consistently. Subsequent quality checks ensure that the translation meets both linguistic and technical requirements.

✅Good to know
Many quality issues arise before the actual translation begins. A clearly worded source text, along with well-maintained terminology, company knowledge and unambiguous style specifications, often improves translation quality more than switching to a more powerful AI model.

Translation quality can be assessed using clearly defined criteria. What matters is not only whether a translation is grammatically correct, but also whether it conveys the content completely, accurately in technical terms, consistently in terms of terminology and appropriately for the target audience. Depending on the type of document, legal, technical or regulatory requirements may also need to be taken into account.

Professional companies therefore assess translations using structured quality criteria rather than the personal language preferences of individual people. This makes translations objectively comparable, comprehensible and continuously improvable.

The most important assessment criteria

• Content accuracy: All information from the source text is transferred completely and correctly, without any loss of meaning or misinterpretation.
• Terminological consistency: Technical terms, product names and company-specific wording are used consistently throughout.
• Linguistic quality: Grammar, spelling, punctuation and readability comply with the linguistic standards of the target language.
• Style and target audience: The tone, writing style and wording are appropriate for the target audience and comply with the company’s style guides and language guidelines.
• Formal and technical requirements: Layout, numbers, units of measurement, references and industry-specific or regulatory requirements are transferred completely and correctly. 

✅ Real-world example

A medical technology company reviews the translation of an instructions for use document. Although the text is grammatically error-free, two approved technical terms are used inconsistently. In addition, a safety-critical warning has been worded too weakly. Only a structured quality review makes these deviations visible and enables targeted correction before the document is published.

✅ Good to know

Many companies now use AI-supported quality checks to assess translations objectively. These checks make it possible to identify terminology errors, inconsistencies or formal deviations much more quickly than through exclusively manual review. However, for safety-critical, legal or regulatory content, these checks do not replace final expert approval by qualified translators or subject-matter experts.

High translation quality is achieved through structured quality assurance measures throughout the entire translation process. These include well-maintained terminology, style guides, translation memories, company knowledge, AI-supported quality checks and – depending on the area of application – a subject-matter review by qualified translators or subject-matter experts.

Quality is not only checked at the end of a translation, but is supported from the preparation of the source material and throughout the entire translation process. The earlier potential deviations are identified, the more efficiently they can be remedied and the lower the subsequent correction effort.

The most important quality assurance measures

• Use terminology consistently: Approved glossaries ensure that specialist terms and corporate language are used consistently in all translations.
• Integrate company knowledge: Style guides, reference documents and translation memories help to apply style, context and existing wording consistently.
• Use AI-supported quality checks: Automated checks identify terminology deviations, inconsistencies, missing content or formal irregularities during processing.
• Plan for human subject-matter review: For legal, technical or safety-related documents, review by qualified translators or subject-matter experts ensures that subject-specific and regulatory requirements are met.
• Continuously improve quality processes: Insights from corrections and completed projects are incorporated into glossaries, style guides and translation processes, thereby improving translation quality in the long term. 

✅Practical example

A software company regularly translates user manuals into ten languages. During translation, the system automatically checks the terminology, identifies missing translations and flags deviations from the company guidelines. Before publication, a subject-matter translator reviews the safety-related content. This allows errors to be identified at an early stage and sustainably improves the quality of the translations.

✅ Good to know

Effective quality assurance does not mean checking every translation completely manually. Modern translation processes combine AI-supported quality checks with a subject-matter assessment by translators or subject-matter experts. This makes it possible to create translations more efficiently without compromising consistency, traceability or subject-matter quality.

Terminology is one of the most important factors for achieving high translation quality. It ensures that technical terms, product names and company-specific wording are used consistently and correctly across all documents. This results in translations that are clear, consistent and technically precise.

If there is no consistent terminology, identical source terms are often translated differently. This leads to misunderstandings, increases the need for corrections and can significantly impair the quality, comprehensibility and traceability of a translation, particularly in technical, medical or legal documents.

The key benefits of consistent terminology

• Consistent technical terms: Approved terms are used consistently in all languages, ensuring technically precise translations.
• Fewer translation errors: Clear terminology specifications reduce misinterpretations and prevent the same term from being translated in different ways.
• Greater efficiency: Translators and AI systems can use approved terms instead of having to research or interpret technical terminology anew for each project.
• Stronger corporate language: Well-maintained terminology supports the company’s language and ensures a consistent brand presence across all languages and communication channels.
• Better AI results: If glossaries are taken into account during the translation process, AI systems can use technical terms much more consistently and appropriately in context. 

✅ Real-world example

A medical technology company defines a binding English translation for a safety-critical technical term. By integrating the terminology glossary, the AI uses this term consistently throughout manuals, instructions for use and package leaflets. This reduces misunderstandings and improves consistency across all documents and target languages.

✅ Good to know

A professional terminology glossary contains much more than simple word translations. It also includes definitions, usage examples, permitted and non-approved terms (deprecated terms), abbreviations and information about the technical context. This information helps both translators and AI systems use technical terms consistently and correctly.

AI-powered quality checks help companies automatically review translations for linguistic, terminological and content-related issues. They identify potential errors during or immediately after translation and help improve quality efficiently, consistently and transparently.

Unlike conventional spelling or grammar checks, modern AI systems also analyse the specialist context, terminology used, writing style and consistency across an entire document. This makes it possible to identify deviations at an early stage and correct them in a targeted manner before they make their way into published content.

The key benefits of AI-powered quality checks

• Early error detection: Terminology deviations, inconsistencies, missing content or formal issues are identified during or immediately after translation.
• Context-based checking: The AI assesses formulations not only linguistically, but also takes the specialist context, target audience and document context into account.
• Consistent quality standards: Translations are objectively checked against defined specifications such as glossaries, style guides and company knowledge.
• More efficient post-editing: Translators and subject-matter experts can focus specifically on the issues identified by the AI, rather than manually checking the entire document again.
• Scalable quality assurance: Even large volumes of documents can be analysed efficiently according to consistent quality criteria.

✅ Practical example

A manufacturer of industrial equipment translates technical documentation into twelve languages. During the AI-powered quality check, the system identifies a terminology deviation, an incorrect unit of measurement and an incompletely translated warning. The proposed corrections are reviewed by the technical editor and incorporated before publication. This enables errors to be rectified at an early stage and the quality of the documentation to be improved sustainably.

✅ Good to know

AI-powered quality checks do not make decisions independently. They identify potential issues and support translators and subject-matter experts with transparent guidance and suggested corrections. The final assessment and approval remain the responsibility of qualified specialists, particularly for safety-critical, technical or legal content.

AI-powered correction suggestions help translators and subject-matter experts resolve identified issues more quickly and precisely. Rather than merely flagging errors, modern AI systems suggest suitable wording while taking terminology, style guidelines, company knowledge and the subject-matter context into account.

This makes post-editing more efficient without transferring responsibility for the final translation to the AI. The suggested changes can be reviewed, adapted or accepted, thereby ensuring a transparent and controllable quality process.

The key benefits of AI-powered correction suggestions

• Specific improvement suggestions: Rather than merely flagging errors, the AI suggests suitable wording as well as terminology and style adjustments.
• Consideration of context: The suggestions are based on the subject-matter context, the target audience, as well as style guides and company requirements.
• Consistent terminology: Glossaries and company knowledge are taken into account to ensure that technical terms and company-specific wording are used consistently.
• More efficient post-editing: Translators and subject-matter experts can focus specifically on the suggested changes instead of manually checking the entire document again.
• Traceable decisions: Every correction suggestion can be transparently reviewed and assessed, adapted or rejected before being accepted.

✅ Practical example

A manufacturer of medical diagnostic equipment is translating an operating manual. During the quality review, the AI system identifies that a technical term does not correspond to the approved terminology and suggests the translation defined in the company glossary. The subject-matter translator reviews and accepts the suggestion, ensuring that the term is used consistently across all documents.

✅ Good to know

The real value of AI-powered correction suggestions lies not in automatically applying changes, but in providing targeted support to qualified editors. The combination of traceable AI suggestions and expert review enables more efficient translation processes and helps improve quality over the long term.

Traceable AI outputs create transparency in the translation process. They show why an AI recommends a term, formulation or correction, and enable translators and subject-matter experts to assess these suggestions on a sound basis. This improves both translation quality and trust in AI-supported translation processes.

Particularly for technical, medical or legal documents, a correction suggestion alone is often not sufficient. Companies need to be able to understand the basis on which a change was recommended and whether it is consistent with terminology, style guides, company knowledge or regulatory requirements. Transparent AI outputs therefore support well-founded decisions and traceable approval processes.

The key benefits of traceable AI outputs

• Greater transparency: Editors can see which specifications, glossaries or company rules underpin the AI’s recommendation.
• Well-founded decisions: Correction suggestions can be assessed objectively on the basis of terminology, company knowledge and subject-matter context.
• Greater trust in AI: Understandable explanations promote collaboration between AI systems, translators and subject-matter experts.
• More efficient quality assurance: Traceable explanations reduce queries and make it easier to carry out the subject-matter review of translations.
• Better documentation: Transparently justified changes support traceable approval and quality processes.

✅ Example from practice

A medical technology company receives a suggestion from the AI to change a technical term in an instruction manual. At the same time, the system indicates that the recommendation is based on the approved company glossary and the requirements of the style guide. The specialist translator can immediately understand, assess and confidently approve the change.

✅ Good to know

Traceable AI outputs are an important part of modern AI governance and the concept of Explainable AI (explainable artificial intelligence). When correction suggestions are transparently justified and linked to company knowledge, this not only improves translation quality. Decisions are also documented more effectively, quality processes become more traceable, and collaboration between translators, subject-matter experts and quality managers becomes more efficient.

Yes. Modern AI systems can now achieve a very high level of translation quality, but they cannot fully replace the professional responsibility of a human. Especially for technical, medical, legal or safety-related content, human quality control remains an important part of the translation process.

AI can create translations, take terminology into account, identify inconsistencies and provide correction suggestions. However, whether a formulation is technically correct, legally unambiguous or appropriate for the company's language should be checked by qualified translators or subject-matter experts. Combining AI with human expertise delivers high efficiency alongside reliable translation quality.

The key benefits of human quality control

• Subject-matter assessment: Subject-matter experts assess whether content is technically correct and suitable for the respective area of application.
• Consideration of context and nuances: People recognise cultural subtleties, linguistic ambiguities and communicative intentions that AI cannot always fully grasp.
• Compliance with regulatory requirements: For medical, technical or legal documents, specialists check whether legal and normative requirements are met.
• Protection of the company's language: Brand messages, tone of voice and corporate language are specifically reviewed and implemented consistently.
• Continuous improvement: Feedback from quality control helps continuously improve glossaries, style guides and AI workflows.

✅ Example from practice

A medical device manufacturer uses AI to translate instructions for use into several languages. The AI creates a pre-translation taking terminology and company knowledge into account. A specialist translator then reviews safety-related information, regulatory wording and linguistic nuances before the document is approved.

✅ Good to know

The international standard ISO 17100 defines requirements for professional translation services. These include, among other things, the revision of a translation by a second qualified person. AI can effectively support and accelerate this process. However, responsibility for the professional review and final approval remains with humans.

Corporate knowledge improves translation quality by providing the AI system with the necessary subject-specific, linguistic and contextual information. This includes terminology, style guides, translation memories, product information and technical documentation, for example. The better these knowledge sources are integrated and maintained, the more consistent and accurate the translation results can be.

Without corporate knowledge, an AI has general language knowledge but does not know a company’s individual requirements. Only by integrating internal knowledge sources can it use technical terms correctly, take the desired tone of voice into account and apply company-specific wording consistently.

The key benefits of corporate knowledge

• Higher terminology consistency: Approved technical terms are used consistently across different documents and languages.
• Consistent corporate language: Tone of voice, writing style and brand messages remain consistent across different content and communication channels.
• Better context processing: Product information, technical documentation and reference texts help the AI classify content more accurately from a subject-specific perspective.
• Less post-editing: Company requirements are taken into account during translation, which can reduce the effort required for subsequent quality checks.
• Sustainable knowledge development: Well-maintained knowledge sources improve the foundation for future translations and ensure more consistent results in the long term.

✅ Example from practice

A mechanical engineering company translates technical manuals into eight languages. In addition to the corporate glossary, translation memories, style guides and technical product documentation are integrated into the translation process. This enables the AI to apply technical terms and the desired writing style more consistently. The subsequent quality check becomes more efficient because many company requirements have already been taken into account during translation.

✅ Good to know

Corporate knowledge encompasses much more than glossaries. Style guides, translation memories, product catalogues, technical documentation, training materials and approved reference translations also help to sustainably improve the quality of AI translations. The more systematically and up to date these knowledge sources are maintained, the more reliably the AI can use them during the translation process.

Style guides improve translation quality by defining clear guidelines for language, tone and writing style. They help both AI systems and translators formulate content consistently and implement the desired corporate language consistently across all languages.

While terminology specifies which technical terms should be used, a style guide describes how content should be formulated. This includes forms of address, sentence length, spelling, addressing the target audience, gender rules and the desired communication style, for example. When these guidelines are consistently followed, translations are linguistically and stylistically compelling and aligned with the brand.

The key benefits of style guides

• Consistent corporate language: Tone, writing style and brand messages remain consistent across documents and languages.
• Improved comprehensibility: Clear language rules support translations that are easy to understand and tailored to the target audience.
• Fewer stylistic deviations: Translators and AI systems follow the same guidelines, resulting in more consistent output.
• More efficient quality assurance: Consistent style guidelines make reviews easier and reduce the effort required for post-editing.
• Stronger brand identity: Consistent language helps companies present a unified brand voice worldwide.

✅ Practical example

A software company publishes its user interface and documentation in several languages. The style guide specifies that users should be addressed formally in German, that short active sentences should be used and that English technical terms should only be adopted in defined cases. The AI takes these guidelines into account during translation. As a result, the user interface, help texts and documentation are linguistically and stylistically consistent.

✅ Good to know

A style guide complements a glossary but does not replace it. While glossaries specify which terms should be used, style guides define how content should be formulated. Only the combination of terminology, style guides and corporate knowledge creates the basis for consistent, high-quality AI translations.

Consistently high translation quality is not achieved through a single AI system, but through the interaction of terminology, company knowledge, style guides, translation memories, quality checks and human expertise. Companies that continuously maintain and develop these building blocks create the foundation for consistent and reliable translations.

Translation quality is not a state achieved once and for all, but a continuous improvement process. New products, technical terms, legal requirements and company policies are constantly changing. Knowledge sources should therefore be updated regularly and the results reviewed systematically.

The most important measures for consistently high translation quality

• Maintain terminology: Regularly add and update new technical terms, product names and approved translations.
• Expand company knowledge: Continuously maintain style guides, translation memories, product information and reference documents.
• Use quality checks: AI-supported quality checks help identify inconsistencies and potential errors at an early stage.
• Involve human expertise: Subject-matter translators and experts review important content and approve translations.
• Continuously improve results: Insights from projects and quality checks are incorporated into glossaries, style guides and other knowledge sources.

✅ Practical example

An internationally operating industrial company regularly updates its corporate glossary, style guides and translation memories after each completed translation project. At the same time, corrections from the quality check are documented and added to the knowledge base. As a result, the quality of AI translations continuously improves, while the effort required for post-editing and corrections decreases.

✅ Good to know

The best translation quality is not achieved through powerful AI models alone. What matters is how well terminology, company knowledge, style guides and quality processes are connected. The more up to date and systematically these knowledge sources are maintained, the more reliably AI systems can use them in the translation process.

ownvia supports companies in sustainably improving their translation quality by combining AI translation with terminology, company knowledge, style guides, translation memories and AI-supported quality checks in a central platform. This enables translation processes to be designed more consistently, transparently and efficiently.

Instead of relying solely on the capabilities of a single AI model, ownvia connects all relevant knowledge sources with the translation process. This allows company-specific requirements to be taken into account during translation and supplemented by AI-supported quality checks and transparent correction suggestions. Professional review and final approval remain with humans, particularly for sensitive or regulated content.

This is how ownvia supports translation quality

• Integrate terminology: Company glossaries support the consistent use of approved technical terms.
• Incorporate company knowledge: Style guides, translation memories, reference documents and other knowledge sources can be integrated directly into the translation process.
• Use AI-supported quality checks: Automated checks help identify terminological, linguistic and formal irregularities at an early stage.
• Provide transparent correction suggestions: Clear recommendations help editors review, adapt or apply changes.
• Support human quality control: Translators and subject-matter experts retain control over the professional review and final approval at all times.

✅ Practical example

A medical technology company translates user manuals into several languages. ownvia combines the company glossary, style guides, translation memories and technical reference documents with AI-supported quality checks. Terminology deviations and stylistic irregularities can be identified at an early stage and supplemented with transparent correction suggestions. Following professional review by the translator, the documents can be prepared consistently and efficiently for publication.

✅ Good to know

High translation quality is not achieved through powerful AI models alone. What matters is the interaction of terminology, company knowledge, style guides, translation memories, defined quality processes and human expertise. ownvia brings these building blocks together in a central platform and supports companies in making their translation processes more structured, consistent and transparent.

When using AI-assisted translations, confidential company information or personal data may be processed. Companies should therefore take data protection and IT security into account when designing their translation processes.

ownvia uses server infrastructure from Microsoft Azure for AI-supported processing within the European Union.

The ownvia platform is hosted on Hetzner servers within the European Union. This means that the hosting infrastructure also remains within the EU.

The company data and translation content processed via ownvia are not used to train AI models.

Temporarily provided content, such as style guides or reference content, may be used for the respective processing and is then deleted – after 24 hours at the latest.

ownvia processes company data within defined technical processes and access rights.

Which data may be processed with ownvia depends on the company's respective internal data protection, security and compliance requirements.

Companies should check whether documents contain personal data or particularly sensitive data and which requirements apply to its processing.

ownvia logically separates the data of different customers. Users can only access the data and content for which they are authorised within their company or organisation. They cannot access other customers’ data.

ownvia is designed as a SaaS solution and is operated on servers within the European Union. A local on-premises installation is currently not planned.

Short answer: ownvia combines generative AI, company knowledge and professional translation processes on a central platform. Instead of limiting itself to a single AI model or an isolated translation service, ownvia enables companies and language service providers to configure AI translations centrally, enrich them with their own specifications and integrate them flexibly into existing system landscapes.

Why ownvia?

• Company knowledge becomes part of the translation: Prompts, terminology and other translation knowledge are specifically incorporated into the AI translation. This creates not a general-purpose translation, but a precisely tailored translation process.

• Centralised control instead of individual AI use: Translation Templates bring translation specifications together centrally. Teams, translators and connected systems can reliably access defined configurations for different customers and use cases.

• Model-agnostic instead of rigidly tied to one model: Different AI models can be used flexibly depending on the use case. The focus is not on a single model, but on the process defined for your company.

• Existing systems remain in place: CAT tools, TMSs, content systems and established workflows do not need to be replaced. ownvia integrates seamlessly as a central AI layer.

• Integration instead of an isolated solution: AI translations are integrated directly into existing workflows via the ownvia API and integrations such as the ownvia AI Translation Provider for Trados Studio.

• Automation and quality assurance combined: Translation processes can be automated, while review, checking and approval steps remain flexibly embedded wherever they are required.

✅ Real-world example:

A company already has a TMS, CAT tools, its own terminology and established approval processes. At the same time, it wants to use modern AI models. Instead of replacing existing systems or carrying out translations with uncontrolled AI tools and individual prompts, ownvia is integrated as a central AI layer. Translation specifications are managed centrally, made available for different use cases and integrated into existing processes.

💡 Good to know:

The key added value of ownvia does not lie solely in the AI model used. Models continue to evolve and can be replaced. What matters is how companies structure their translation specifications and knowledge in conjunction with AI and integrate them into their existing processes in a controlled manner. That is precisely what ownvia provides: a central platform.

Short answer: An integration connects AI translation functions directly to existing systems and workflows. This allows content to be processed where it is created, without having to transfer it manually between different applications.

The key types of integration at a glance

• API interfaces: AI translation functions are called directly from other applications and systems.
• Plugins and connectors: Existing software is enhanced with AI-powered translation functions.
• Automated workflows: Content can be automatically submitted for translation, processed and then handled further.
• Workflow integration: AI is integrated into existing processes without having to replace existing systems entirely.

  ✅ Example from practice

A company manages product information in a PIM or content system. Via an interface, texts can be transferred to an AI translation platform, processed in accordance with defined translation requirements and then returned to the existing process.

    💡 Good to know

An AI translation platform does not have to replace existing systems. Interfaces and integrations can be used to specifically enhance existing processes with AI-powered translation functions.

Short answer: ownvia can be integrated into existing translation and business processes via interfaces and integrations. This includes, in particular, the ownvia API as well as direct integration into Trados Studio.

The key integration options at a glance

• ownvia API: The interface allows AI-powered translation functions to be integrated into your own applications, systems and automated workflows.

• Trados Studio integration: The ownvia AI Translation Provider enables ownvia to be used directly within the familiar Trados working environment.

• Translation Templates: Translation Templates configured centrally in ownvia can be used via the Trados integration and can, for example, take prompts, terminology and other configured translation knowledge into account.

• Individual workflows: Interfaces allow ownvia to be integrated into existing content and translation processes.

✅ Example from practice

A language service provider or in-house translation team can continue working in Trados Studio and use the Translation Templates centrally configured in ownvia there for AI translation. Alternatively, the API can be used to integrate translation functions into your own applications and processes.

💡 Good to know

ownvia does not need to replace existing software. The platform can selectively enhance existing tools and processes with centrally controlled AI-powered translation functions.

Short answer: Yes. The ownvia API enables AI-powered translation functions to be integrated directly into existing applications, systems and automated workflows.

What does the ownvia API enable at a glance?

• System integration: ownvia can be connected to existing business applications and custom software solutions.
• Automated processing: Content can be automatically submitted for translation via the API, processed and then passed on for further processing.
• Centralised translation specifications: During processing, Translation Templates configured centrally in ownvia can be used with prompts, terminology and other translation knowledge.
• Workflow integration: AI translations can be used as an integral part of existing processes.

✅ Practical example

A company wants to translate product texts from its own software or database. The system transfers the content to ownvia via the API. There, it is processed in accordance with the configured translation specifications and then returned to the connected system.

💡 Good to know

The API can be used to automate translation processes without users having to manually start each translation process via the ownvia user interface.

Short answer: Yes. ownvia can complement existing translation processes and flexibly integrate AI-assisted translation functions into current workflows. Existing systems and familiar working methods do not need to be replaced.

How can ownvia complement existing translation processes?

• Keep using existing tools: Integrations such as the ownvia AI Translation Provider allow ownvia to be used directly in familiar environments such as Trados Studio.

• Configure AI centrally: Translation Templates allow prompts, terminology and company knowledge to be managed centrally in ownvia and made available across processes.

• Automate processes: The ownvia API allows recurring translation steps to be embedded directly into existing workflows.

• Retain human review: Established review, correction and approval processes can remain part of the translation workflow.

✅ Practical example:

A translation team continues working with its established processes in Trados Studio. AI translation is integrated through ownvia, while translators and subject-matter experts can retain their familiar quality and approval processes.

💡 Good to know:

ownvia is designed to complement existing structures. AI can be integrated specifically where it supports and accelerates existing translation workflows.

Short answer: Yes. With the ownvia AI Translation Provider, AI-powered translations from ownvia can be used directly in Trados Studio. This allows translators to continue working in their familiar Trados environment while using the AI translation centrally configured in ownvia.

How does the integration with Trados Studio work?

• Direct use in Trados: ownvia is integrated into the existing Trados workflow as a Translation Provider.

• Central Translation Templates: The templates used for translation, including prompts and model selection, for example, are managed centrally in ownvia.

• Company-specific requirements: Terminology and other configured translation knowledge can be taken into account for AI translation.

• Familiar working environment: Translators can continue using the familiar features and processes in Trados Studio.

✅ Practical example:

A translation service provider works with Trados Studio and wants to use AI translations with customer-specific requirements. Via the ownvia AI Translation Provider, they select the appropriate Translation Template from ownvia and translate directly in Trados—without having to leave their familiar workflow.

💡 Good to know:

The Translation Templates are managed centrally in ownvia. This makes it possible to provide consistent translation requirements while translators continue working in their familiar Trados environment.

Short answer: Yes. With ownvia, recurring translation processes can be automated and integrated into existing system and content workflows. Via the ownvia API, content can be submitted for AI translation and subsequently processed further.

Which translation steps can be automated?

• Submit content: Texts and content can be automatically transferred from connected systems to ownvia.

• Perform translation: The content is processed using the translation instructions configured for the respective process.

• Use Translation Templates: Prompts, terminology and other translation knowledge can be centrally provided for AI translation.

• Process results further: Translated content can then be further processed in the connected systems or workflows.

✅ Example from practice

A company regularly publishes new product information. Instead of manually transferring the content to a separate translation system for each language, translation can be integrated into the existing process via an API integration. ownvia processes the content using the specified translation instructions and makes the translations available for further processing.

💡 Good to know

Automation does not mean that people disappear from the process. Depending on the use case, review, checking or approval steps can continue to be integrated into the automated workflow.

Short answer: No. ownvia complements existing translation tools and processes. Companies and language service providers can continue using their existing systems and integrate AI-assisted translation functions where needed.

How does ownvia complement existing translation tools?

• Keep using existing systems: Existing CAT tools, TMS or content systems do not generally need to be replaced.

• Integrate AI functions: Interfaces and integrations allow ownvia to be incorporated into existing processes.

• Connect Trados Studio: The ownvia AI Translation Provider allows ownvia to be used directly within Trados Studio.

• Control AI centrally: Translation Templates allow prompts, terminology and other translation knowledge to be managed centrally in ownvia.

✅ Practical example:

A language service provider already uses Trados Studio and established review and approval processes. Instead of replacing this infrastructure, they integrate ownvia for AI-assisted translation and continue carrying out quality assurance through their existing processes.

💡 Good to know:

ownvia can be integrated as a central AI layer into an existing translation environment. This allows existing systems and working methods to be retained and selectively extended with AI-assisted translation functions.

Short answer: ownvia can be connected to various business and translation systems via interfaces and integrations. These include content systems, translation tools and bespoke software solutions.

Which systems are suitable for integration?

• CAT tools: ownvia can be integrated into existing translation environments, for example via the ownvia AI Translation Provider in Trados Studio.

• Content and product information systems: Content from CMS, PIM or comparable systems can be integrated into translation processes via interfaces.

• TMS and translation platforms: ownvia can be integrated as an AI component into existing translation workflows.

• Bespoke business software: The ownvia API can also be used to connect custom applications and processes.

✅ Example from practice

A company manages its product information in a PIM system. Via an API integration, relevant content can be transferred to ownvia, processed according to the specified translation requirements and then reused in the existing process.

💡 Good to know

The appropriate form of integration depends on the existing system landscape and the respective translation process. ownvia can be integrated into existing environments both via available integrations and via the API.

Short answer: ownvia supports common Office files as well as professional CAT exchange formats such as XLIFF and SDLXLIFF.

The most important formats at a glance

• Office files: Word, Excel and PowerPoint.

• XLIFF & SDLXLIFF: Exchange formats for integration into professional translation and CAT workflows.

• ownXLIFF: ownvia-proprietary format for the structured further processing of translation content and additional translation information.

💡 Good to know

By supporting XLIFF and SDLXLIFF, ownvia can also be integrated into existing CAT-based translation processes.

Short answer: Yes. For supported file formats, ownvia specifically processes the translatable content while preserving the document structure for the output.

What is taken into account?

• Document structure: Paragraphs, tables, lists and other structural elements are retained.

• Formatting: Formatting information is taken into account during processing.

• Reuse: The translated content can then be reused in the corresponding document format.

Short answer: Yes. In ownvia, multiple files can be uploaded and processed together for a translation. This also enables larger quantities of documents to be processed efficiently in a single translation workflow.

What does this mean for the translation process?

• Multiple documents: Files can be provided together for a translation order.

• Consistent specifications: The selected translation specifications can be applied centrally to the files in the translation process.

• Joint processing: Instead of translating documents individually, multiple files can be processed in a single operation.

Short answer: Yes. ownvia supports exchange formats such as XLIFF and SDLXLIFF and can therefore process translation files from professional CAT workflows.

What does this mean for existing translation processes?

• XLIFF: Standardised XLIFF files can be used for further processing in ownvia.

• SDLXLIFF: Files from Trados Studio can be processed in ownvia and then reused in the translation workflow.

• Existing workflows: Companies and language service providers can continue using their existing CAT processes and integrate ownvia specifically for AI-assisted translation.

Short answer: ownXLIFF is an XLIFF variant adapted for ownvia for the structured processing of translation data. In addition to source and target texts, it can also carry additional information from the ownvia translation process.

What is ownXLIFF used for?

• Structured translation data: Source texts and translations are stored in segments in a single file.

• Further editing in ownEDITOR: ownXLIFF files can be opened directly in ownEDITOR and edited further.

• Process information: Additional information from the ownvia configuration is retained together with the translation data.

Short answer: Yes. Existing translations can be transferred to ownvia and used for further editing. This means that an existing target text does not have to be translated from scratch.

What is this useful for?

• Continue editing existing translations: Existing target texts can be integrated into the translation process and edited in ownEDITOR.

• AI-supported optimisation: Existing translations can be specifically revised and optimised using the functions of ownvia.

• Professional workflows: Existing translation versions can be integrated into further review or editing processes.

• Continue using existing resources: Companies and language service providers can continue using existing translations instead of having to recreate translation content for each process.

Short answer: The ownEDITOR is the professional working environment from ownvia for editing and reviewing AI-assisted translations. Translators, reviewers and companies can review and edit translations segment by segment and further optimise them with the AI features of ownvia.

What does the ownEDITOR offer?

• Structured editing: The source text, existing translation and edited translation are clearly displayed side by side and edited segment by segment.

• AI support: Translations can be reviewed, revised and optimised directly in the editor with AI assistance.

• Terminology in context: Stored terminology is taken into account directly during the translation process and made visible in the editor.

• Document context: When editing, the AI can take additional context from the document into account instead of considering individual segments exclusively in isolation.

• Professional post-editing: Human review and AI assistance are combined in a shared working environment.

💡 Good to know

The ownEDITOR is not designed to replace translators or reviewers. It supports them in efficiently reviewing, editing and further developing AI-generated and existing translations according to defined specifications.

Short answer: During post-editing in the ownEDITOR, AI-generated or existing translations are checked segment by segment and revised where necessary. Translators and reviewers retain full control over the final translation while also using the AI functions of ownvia for targeted editing.

How does the ownEDITOR support post-editing?

• Direct comparison: The source text and translation are clearly displayed side by side.

• Segment-by-segment editing: Translations can be specifically checked, corrected and adjusted.

• AI support: If desired, the AI supports the reformulation or revision of individual segments.

• Terminology check: Stored terminology is displayed directly in the editor to help identify deviations.

• Human control: Translators and reviewers make the final decision on which changes are adopted.

Short answer: In ownEDITOR, AI directly supports the review and revision of translations. Users can edit individual segments specifically with AI and use the existing translation context as well as the specifications stored in ownvia.

What AI support does ownEDITOR offer?

• Revise translations: Individual segments can be rephrased, optimised or adapted to specific requirements with AI support.

• Translation Chat: Questions about individual translations can be clarified directly with AI during the translation process in order to develop targeted alternative formulations.

• Consider context: Additional document context can be included during editing so that translation decisions are not limited exclusively to individual segments.

• Include terminology: Stored terminology can be taken into account during AI-assisted editing and viewed directly in the editor.

• Human decides: AI suggestions support the editing process, while translators and reviewers decide on the final wording.

Short answer: The ownEDITOR helps translators and reviewers take stored company terminology into account directly during editing. Terms are highlighted in the editor, and potential terminology deviations can be specifically identified and reviewed.

How is terminology supported in the ownEDITOR?

• Terminology directly in the editor: Stored terms and their specified translations are clearly displayed during editing.

• Identify deviations: The ownEDITOR flags segments in which stored terminology has not been used as specified.

• Review specifically: Segments with terminology deviations can be isolated using filters and edited efficiently.

• AI support: Stored terminology can also be taken into account when revising translations with AI assistance.

• Human control: Translators and reviewers check the suggested terminology in context and decide on the final wording.

Short answer: The Translation Chat is an AI assistant integrated directly into ownEDITOR for working on translations. Users can ask questions about individual translation segments, review wording and develop alternative translations without leaving the translation process.

What can the Translation Chat be used for?

• Questioning translations: Users can have individual translation decisions explained and ask targeted follow-up questions about a segment.

• Alternative wording: On request, the AI suggests different translation or wording variants.

• Revising translations: Individual segments can be specifically adapted or reformulated based on concrete instructions.

• Taking terminology into account: Stored terminology can be taken into account when working with the Translation Chat.

• Directly within the workflow: The chat is available directly in ownEDITOR, allowing questions and corrections to be addressed during post-editing.

💡 Good to know:

The Translation Chat does not replace the decisions of the translator or reviser. It serves as an AI-assisted tool for reviewing translation decisions, developing alternatives and refining wording in a targeted manner.

Short answer: The ownEDITOR provides features for the structured and efficient editing of professional translations. Translations can be checked, searched and adjusted segment by segment. Additional editing functions support translators and reviewers in their daily work.

Which editing functions are available?

• Segment-by-segment editing: The source text and translation are clearly displayed side by side and can be edited specifically segment by segment.

• Search and replace: Terms and phrases can be specifically searched for and replaced within the translation.

• Editing repetitions: Repeated content can be identified throughout the document and adjusted consistently.

• Bookmarks: Relevant segments can be marked so that they can be quickly found again later.

• Multiple translation files: Multiple ownXLIFF files can be opened simultaneously in the ownEDITOR and edited as part of a single workflow.

Short answer: The ownEDITOR helps translators and reviewers check translations in a structured way and revise them selectively. It combines human quality control with AI support, terminology checking and functions for efficiently editing individual segments.

How can translations be reviewed in the ownEDITOR?

• Direct comparison: The source text and translation are displayed side by side, allowing individual segments to be reviewed and edited selectively.

• Check terminology: Stored terminology is made visible in the editor, allowing potential deviations to be identified and filtered selectively.

• Use AI support: Individual formulations can be checked, revised or rephrased with AI support.

• Translation Chat: Translation decisions can be questioned directly in the editor and alternative formulations can be developed.

• Human approval: Translators and reviewers decide on changes and the final wording of the translation.

💡 Good to know:

The ownEDITOR combines automated support with human review. The final quality assessment therefore remains an integral part of the professional translation process.

Short answer: The ownEDITOR combines professional translation editing with generative AI, corporate knowledge and centrally defined translation guidelines. This means that AI is not used as a separate tool, but is integrated directly into a controllable translation and post-editing process.

What makes the ownEDITOR approach distinctive?

• AI directly in the workflow: Translations can be checked, revised and further refined directly in the editor using generative AI.

• Incorporating corporate knowledge: Stored terminology and other configured translation guidelines can be taken into account during editing.

• Translation Chat: Translators and reviewers can question translation decisions directly in the editor with the AI, develop alternatives and refine formulations precisely.

• Taking context into account: Additional document context can be included in AI-supported editing, so that translation decisions are not based solely on isolated segments.

• Human control: AI supports translation and revision, while translators and reviewers make the final decision on the wording.

• Part of the ownvia platform: The ownEDITOR combines human editing with the translation guidelines and AI functions stored centrally in ownvia.

💡 Good to know:

The key difference does not lie in a single AI function. The ownEDITOR brings generative AI, corporate knowledge and professional human translation work together in a shared working environment. This allows companies and language service providers to use AI without removing their subject-matter guidelines and human control from the translation process.

An activation is a single processing step in which content is used for a specific function, for example for a translation, analysis or a micro-app.

When a document is translated, activations are generated depending on the scope of the document, the selected language and the function used. Micro-apps can also use activations, for example when they check, summarise, structure or transfer texts into a specific format.

In short: An activation describes how often content is technically processed so that the desired service can be provided.

To help you better estimate the costs of translating a document in advance, we provide an activation analysis tool. There, you can approximately calculate how many activations are required for your document.

Activation analysis tool

This gives you a transparent initial estimate before you use a translation or micro-app function.

In ownvia enterprise, your data remains secure and under your control at all times.

• All data is processed exclusively on EU servers.
• No data is stored permanently – except for data that you intentionally upload to train your micro-apps.
• Daily deletion routines ensure that all other information is automatically removed.
• You decide: You can download results at any time, continue editing them locally, or upload them to ownvia again for further processing.

This means you retain full control – without data remnants in the cloud.

As a rule, you create your micro-apps yourself – simply, quickly and without any programming knowledge. Most of our customers use this option independently.

However, if you need support or would like a specially customised micro-app, you can book our expertise at any time. On request, our team develops tailored solutions or supports you in creating them – this service is optional and charged separately.

A micro-app is a specialised AI assistant designed to efficiently perform a specific task or narrowly defined workflow. On our platform, you or the ownvia team can transform specific AI prompts into such assistants and add company-specific information so that they can work with it optimally.

Unlike comprehensive software solutions that offer a wide range of functions, micro-apps focus on the standardised execution of very specific functions or processes.

Yes, ownvia supports SDLXLIFF files directly. You can translate these files, review existing translations and, if necessary, automatically correct them according to defined specifications.

The review of a translation can be organised in ownvia using the proprietary exchange format ownxliff. Project participants can be invited to the ownvia Enterprise Server free of charge to complete tasks together quickly and easily.

In addition, ownvia generally supports the XLIFF format, making it compatible with files from many other CAT tools as well.