Large language model - use of LLMs in SMEs

The use of large language models (LLMs) in SMEs is becoming increasingly important. These artificial intelligences, which are based on the understanding and generation of natural language, offer a wide range of possible applications - from intelligent text analysis to the creation of new content with GenAI through to automated translation.

In a current example project, a medium-sized plant manufacturer is optimizing its knowledge management by implementing such a model. The company uses the advanced technology to analyze unstructured data from various sources and make it accessible.

This development impressively demonstrates how artificial intelligence and machine learning can advance SMEs by making processes more efficient and increasing innovation potential.

What is a large language model?

Definition - A large language model is an artificial intelligence (AI) based on the processing and understanding of natural language. These models, known as Large Language Models (LLMs), can read, understand and generate text by accessing large amounts of data and complex algorithms. They are used for a variety of applications, including text analysis, translation and question answering. By training on large data sets, these models (model learning) learn to recognize patterns and relationships in language, which makes them particularly powerful.

Challenge

Our example company in this use case is a medium-sized plant manufacturer that produces components from technical textiles. In this industry, the specialist knowledge of employees is a critical success factor. However, this knowledge is often contained in unstructured data, consisting of paper-based and digitalized documents and the minds of employees. This fragmentation of knowledge poses an enormous challenge, especially when it comes to securing and making know-how accessible to the next generation.

Objective

To overcome this challenge, the company decided to work with a specialized LLM AI partner, in this case the Helm & Nagel GmbH, with its AI product Konfuzio. The aim of the cooperation is to analyze the existing but scattered documents and their unstructured data and make them usable. A large language model will be used to significantly improve knowledge management.

Implementation

The implementation of the solution takes place in several steps and is divided into:

Large language model - analysis and preparation

The LLM AI partner begins with a detailed analysis of the existing data sources, which include PowerPoint presentations, Word documents, PDFs, Excel docs and other text files. The aim is to develop a customized solution based on the specific needs of the plant manufacturer. Generating an analysis of these values is one of the most important tasks in the run-up to the development and implementation of a language model.

Large language model - implementation

The solution is also implemented in several steps:

System development - Development of a system that answers questions based on the existing knowledge in the documents using artificial intelligence. The system searches through all relevant documents and provides precise answers, while also offering the option of accessing the original sources.

LLM Integration - The large language model is then integrated in order to support non-German-speaking employees with the technology. Translation functions play a central role here. Language models such as this one are able to understand and process large amounts of information in different languages.

Audio transcription - An audio transcription function is added to the Large Language Model (LLM) to automatically capture verbal knowledge and generate a transcript. This enables learning and comprehensive understanding during meetings or informal conversations within production. Structured documentation of the content allows it to be used at a later date.

Large language model - applications and advantages

Efficient knowledge management

By using a large language model, the existing knowledge is structured and made more easily accessible. Employees are able to ask specific questions and receive precise answers that are generated directly from the existing documents. This saves time and significantly reduces the effort required to search for knowledge.

Overcoming language barriers

The implementation of translation functions in the LLM solution supports international teams. Technical information and texts are translated into different languages, which facilitates collaboration and knowledge transfer across language boundaries.

Capturing tacit knowledge

Audio transcription also makes it possible to record non-documented knowledge. Verbally expressed information, which is exchanged in officially scheduled meetings or spontaneous conversations, but only in the context of business activities or communications, is now also part of the knowledge database and is available for later queries.

Flexibility and adaptability

The LLM solution is adapted to the individual needs of the plant manufacturer and forms the basis for reacting quickly to changes. The system is suitable for continuous expansion and improvement to meet growing requirements.

Hurdles and success factors during implementation

When implementing a new technology, such as large language models, in existing structures, there are various challenges that need to be overcome:

• Integration - Seamlessly integrating the new technology into the company's existing systems and processes is a challenging task.
• Training and acceptance - Employees must be trained to use the new technology effectively. It is also important to ensure a high level of acceptance of the new solution throughout the company.
• Data quality and consistency - In order to achieve precise and reliable results, the quality and consistency of the data must be ensured.

The successful implementation of the project is based on several success factors:

• Transparent communication - An open exchange and mutual trust between the plant manufacturer and the LLM-KI partner are crucial. Transparency in the project steps and clear communication of the objectives contribute significantly to success.
• Pragmatic approach - The focus on specific use cases and iterative improvements helps to integrate the LLM technology step by step and achieve measurable success quickly.
• Long-term perspective - The willingness to engage in long-term collaboration and continuous learning is another important factor. Permanent adaptation and ongoing improvement of the solution, based on user feedback, supports the benefits of implementation in the long term.

Large language model - applications and further development

The solution is now in the final stages:

Validation phase

The model is validated, continuously adapted and improved. Feedback from users plays a key role in further optimizing the model solution.

Long-term relationship

The aim is to maintain a long-term partnership in order to continuously make new improvements and adjustments. This ensures that the solution always meets current requirements.

Feedback and optimization

Regular user feedback is used to optimize and identify new areas of application. This promotes the continuous improvement and adaptation of the technology to the changing needs of the company.

Conclusion

The cooperation between the medium-sized plant manufacturer and the LLM AI company impressively demonstrates how large language models can be used effectively in medium-sized companies. By using LLMs, knowledge management can be significantly improved, processes optimized and the ability to innovate increased. The transparent and pragmatic approach as well as the long-term perspective are important factors. Success factors for the project.

The implementation of large language models opens up new opportunities for companies to manage and use their knowledge more efficiently. With the continuous development of this technology, small and medium-sized enterprises (SMEs) will continue to benefit from the advantages of the artificial intelligence process.

The role of Large Language Models (LLMs) in task management

Large language models take on a variety of tasks in the company. These technological helpers analyze large amounts of data and extract valuable information from it. Understanding and generating language enables LLMs to provide answers to complex questions and thus support knowledge management. These tasks include analyzing texts, translating documents and transcribing spoken content. Continuous development of these large language models constantly increases the efficiency and accuracy of these tasks.

Large language model as a future technology for SMEs

The applications and potential of large language models in SMEs are diverse. With advancing development in the Machine Learning (ML) and artificial intelligence, these language models are becoming increasingly powerful and adaptable. By using LLMs, companies can not only optimize their internal processes, but also tap into new business areas.

The continuous improvement and adaptation of these models to the specific needs of SMEs ensures that companies remain competitive in the future and benefit from the advantages of digitalization.