In 2025, multilingual data becomes smarter and the localization industry and how to improve the quality and efficiency of localization services?

Multilingual data intelligence

• More advanced technology applications: NVIDIA's NeMo Retriever microservice can understand data in multiple languages and formats and convert it into text to help achieve context-aware results. It converts information into a language that can be understood by artificial intelligence and inserts it into a vector database through operations such as conversion and extraction, thereby improving data retrieval accuracy.
• Multimodal fusion development: In the future, multilingual data intelligence will not only be limited to text, but will also integrate multimodal data such as images, PDFs, and videos. For example, NVIDIA has been studying the application of multimodal data in NeMo Retriever to enrich the application scenarios of multilingual data.
• Personalization and adaptation: With the development of natural language processing technology, multilingual data intelligence will be more personalized and intelligent, and can provide accurate services based on user habits and needs. For example, intelligent customer service can adjust the answer strategy based on user questions.

Localization industry forecast

• Demand continues to grow: Enterprises are accelerating their overseas expansion, cross-border trade and cultural exchanges are frequent, and the demand for localization of product digital content is increasing. In order to expand overseas markets, various industries need to overcome cross-language barriers, and the demand for localization services continues to be strong.
• Deepening cross-industry integration: Localization services have strong scalability and will be combined with the financial industry, media industry, and traditional manufacturing industry. Deep integration, forming cross-border cooperation and ecological co-construction, sharing resources, optimizing configuration, and improving the competitiveness of the service ecosystem.
• Improvement of quality and efficiency: The development of digitalization and artificial intelligence has prompted localization service providers to improve service quality and efficiency. Customized machine translation engines in professional fields have become more mature. Cloud computing and big data can help analyze demand trends and achieve product innovation and service upgrades.
• Improvement of industry norms and standards: Improvement of regulatory standards for specific industries, such as finance, environmental energy, life sciences, etc., has prompted enterprises to improve the quality of digital content localization services, promote the expansion of the localization service market, and provide standardized services that comply with regulations to become the key to customer acquisition for service providers.

Improve the quality of localization services

• Standardize workflows

Develop detailed localization workflows, including standard operating procedures for translation, editing, proofreading, testing, and other links. For example, in the translation link, translators must use designated glossaries and translation memories to ensure terminology consistency and translation accuracy.

Clearly define the quality inspection standards for each link, such as the translation quality must reach a certain accuracy rate, and the localized software or content must pass specific functional tests and user experience tests. For work results that do not meet the standards, there must be a clear rework process.

• Application of technical tools

Use a localization management system (LMS) to manage localization projects and automate functions such as task allocation, progress tracking, and resource management. For example, through LMS, you can intuitively see the progress of each translation task and the workload of the translator, so as to facilitate timely adjustment of task arrangements.

Use machine translation engines as auxiliary tools, but pay attention to manual editing and quality control of machine translation results. For example, for some highly repetitive and formatted documents, such as product manuals, machine translation can be used first, and then polished and reviewed by humans, which can greatly improve translation efficiency.

Establish a good partnership

• Supplier cooperation

Establish a long-term and stable cooperative relationship with high-quality translation service providers, localization testing agencies, etc. When selecting a supplier, you should conduct a comprehensive assessment of its qualifications, service quality, price, etc. For example, when selecting a translation supplier, check the qualification certificates of its translator team, previous translation work cases, etc.

Communicate with suppliers regularly and jointly develop quality improvement plans to ensure that the services they provide meet the requirements of the localization project. For example, work with the testing agency to determine test cases and test standards to improve the quality of localized products.

• Customer cooperation

During the localization service process, maintain close communication with customers and let customers participate in key decision-making links, such as the formulation of service plans and the review of important content. For example, when localizing a website for a company, let the company's marketing department participate in the website interface design and content review process to ensure that the localized website meets the company's brand image and market positioning.

Provide value-added services, such as maintenance and update support after localization, to enhance customer satisfaction and loyalty. For example, for localized software, provide regular update services to fix possible localization problems, such as language display errors.