Scenario: As a production manager in a factory, you have a question about safety regulations for a specific machine part. In the past, you had to manually search through manuals and safety reports, which could be time-consuming and frustrating.

Now, you simply ask the question in the chat interface: "What are the safety regulations for working with the XYZ machine?" Within seconds, the LLM system analyses your question, searches for relevant information in the stored documents, and presents you with a clear and concise answer.

Scenario: As a customer service representative, you deal with a wide range of questions about products, services, policies, and procedures daily. In the past, you had to find quick and accurate answers by searching through manuals, internal documentation, or previous customer communications yourself. This could be stressful and often took a lot of time.

Now, the chat feature is integrated with a database of relevant company information, allowing you as a customer service representative to quickly and easily find the necessary information to help customers. This leads to faster solutions, higher customer satisfaction, and more efficient customer service.

Scenario: As a procurement specialist, you used to often need quick access to information about suppliers, prices, product details, and delivery terms, which meant digging through stacks of paper or digital documents yourself. This could be time-consuming and error-prone.

By connecting the chat feature to a database with supplier information, contracts, and purchase orders, you can now efficiently find information and make better-informed decisions about supplier selection, price negotiations, and inventory management.

Scenario: As a financial analyst, you work with large amounts of financial data, such as revenue, costs, and profitability. In the past, you had to perform time-consuming analyses and apply complicated formulas in Excel or SQL to answer questions about this data.

Now, you simply ask the question in the chat interface: "What is the average revenue per customer for the past quarter?" Within seconds, the LLM system analyses your question, searches for relevant information in the stored numerical data, and presents you with a clear and concise answer.

Scenario: As a facility manager, you need to oversee the maintenance and repair work of buildings and equipment within your organisation. In the past, you had to keep track of which tasks were completed and which were still in progress and browse through maintenance reports to determine the status of different projects.

Now, you simply ask the question in the chat interface: "What is the current status of the air conditioning maintenance in building B?" The LLM system analyses your question, searches for relevant information in the stored numerical data, and presents you with a clear and concise answer so that you can make better-informed decisions about facilities management.

Scenario: As a logistics manager, you need to quickly respond to questions about stocks, deliveries, and transportation routes. In the past, you had to log into different systems and browse through tables to find the necessary information. This could be time-consuming and error-prone.

By linking the chat function to a database with inventory information, supplier data, and transportation data, you can now efficiently find information and make better-informed decisions about inventory management, delivery times, and logistics planning.

Scenario: As an HR staff member, you manage a lot of data about employees, such as years of service, salary data, leave and sickness registrations, and performance reviews. Previously, answering questions about this data was a challenge because you had to search through various files, spreadsheets, or HR systems yourself. This could be time-consuming and increase the risk of errors.

Now, you can simply ask a question in the chat interface, such as "How many employees have more than 5 vacation days left this year?" or "What is the average salary of our employees in department X?". The LLM system analyses your question, searches for the relevant information in the stored numerical data, and provides an accurate and understandable answer. This makes it easier to quickly gain insight into HR data, make better decisions, and communicate more effectively with employees and management.

Scenario: As an urban planning policy maker, you are working on a plan for the development of a new city district. You want to know which strategies and planning principles have been successful in similar urban projects.

Instead of going through numerous reports and case studies yourself, you simply ask the question in the search function: "Which successful urban development strategies have been applied in similar city districts?" The LLM system analyses your question, searches for relevant information in the stored publications, and presents you with an overview of the most successful strategies. This allows you to make better informed decisions when developing the new city district.

Scenario: As a social science researcher, you are working on a study about the effects of social media on the well-being of young people. You want to know which recent studies have been conducted on this subject and what methods and measuring instruments have been used.

Instead of manually browsing through numerous publications, you simply ask the question in the search function: "Which recent studies have been conducted on the effects of social media on the well-being of young people, and what methods and measuring instruments have been used?" The LLM system analyses your question, searches for relevant information in the stored publications, and presents you with an overview of the found studies and their methodologies. This helps you refine your own research methods and contribute to the existing scientific debate.

Scenario: As a legal professional, you are researching the legislative history and relevant parliamentary documents for a better interpretation of a legal provision.

Instead of manually browsing through documents, you simply ask the question in the search function: "What is the legislative history and relevant parliamentary documents related to legal provision X?" The LLM system analyses your question, searches for relevant information, and presents you with an overview of the found results. This helps you better understand the intent of the legislator and more efficiently construct legal arguments.