What are chatbots and how do they work?

What is a chatbot?

Chatbots are a form of automated service. A robot, so to speak, with which customers can communicate via a chat. They are based on artificial intelligence (AI) that works through a set of defined rules and parameters. Basically, you have a virtual conversation with a robot that can perform certain tasks for you or answer your questions.

While earlier chatbots were often rule-based and could only provide predefined answers, in 2025 AI-supported chatbots will be able to analyse complex requests, provide personalised answers and learn from previous interactions. They will be used in areas such as customer support, human resources and knowledge management.

Where Will Chatbots Be Used in 2025?

Chatbots are becoming increasingly prevalent across various industries:

• E-Commerce – In online stores, chatbots act as virtual sales advisors, enhancing the shopping experience through personalized product recommendations and voice search. With AR integration, customers can view products in 3D or conduct virtual try-ons via chatbot interaction.

• Banking & Finance – Financial chatbots analyze account activities, provide personalized saving tips, and assist with transactions. They detect suspicious activities in real time and offer secure identity verification through voice recognition or multimodal biometrics.

• Healthcare – AI-powered chatbots support doctors and patients by functioning as symptom checkers, medication reminders, and automated appointment schedulers. Advanced medical LLMs like DeepSeek-Vision can even assist with medical image analysis and serve as an initial point of contact for diagnoses.

• Education – Chatbots act as personalized learning assistants in schools, universities, and online courses. They adapt to the user’s learning level, provide interactive tutoring in real-time, and generate AI-powered educational materials and multimodal learning content.

• Public Administration – Governments and municipalities utilize chatbots for digital citizen communication. They assist with applications, tax-related queries, and traffic information. In smart cities, intelligent urban assistants help with navigation, parking searches, and real-time environmental updates.

Key Use Cases for Chatbots are for example customer service & human resources:

• Customer Service – Chatbots handle automated customer interactions and provide 24/7 support via websites, messaging apps, and phone services. Thanks to enhanced personalization and improved natural language understanding, they can resolve complex issues, reduce escalations, and decrease wait times in call centers.

• Human Resources – In internal communication, chatbots streamline HR processes by automatically answering employee inquiries, guiding onboarding procedures, and suggesting training opportunities.

The Popularity and Benefits of Chatbots

The popularity of chatbots continues to grow steadily in 2025 as companies recognize the advantages of this technology.

The increasing adoption of chatbots is driven by businesses realizing their potential and leveraging their benefits. With ongoing advancements in artificial intelligence and machine learning, chatbots now offer unprecedented opportunities to optimize business processes and enhance customer interactions. Some of the key advantages that motivate companies to implement chatbots include:

• Cost Reduction and Efficiency Improvement: Chatbots automate a wide range of repetitive tasks that previously required manual effort, such as customer support, appointment scheduling, or frequently asked questions. This leads to a significant reduction in operational costs and allows employees to focus on more complex tasks. Chatbots operate 24/7, increasing efficiency and service availability.
• Enhanced Customer Experience: Chatbots provide instant responses to inquiries, significantly improving user experience. They can process requests quickly and offer personalized support by delivering real-time information about products, services, or processes. This fast and accessible communication contributes to higher customer satisfaction.
• Scalability: Chatbots can handle a large number of customers simultaneously without compromising service quality. This is particularly beneficial during peak times or when processing large amounts of data. Companies can expand their capacity without the need for additional resources.

Due to these and many other advantages, more and more businesses recognize the necessity of integrating chatbots into their business strategies. This technology is no longer just an innovation but an essential component for improving efficiency, customer satisfaction, and competitiveness in an increasingly digital world.

LLM Chatbots (Large Language Model Chatbots)
LLM chatbots are based on advanced language models such as GPT-4 and rank among the most powerful chatbot types. They use deep machine learning and extensive training data to understand and generate human-like text. These chatbots can conduct complex conversations, understand context, and provide coherent, relevant responses. They have proven to be highly useful in areas such as customer support, content generation, and interactive user interfaces. With their continuous improvement, they can create dynamic and personalized experiences.

Rule-Based Chatbots
Rule-based chatbots follow a strictly defined set of rules and can only respond to predefined inputs. They specialize in simple queries, such as answering frequently asked questions or checking order statuses. These chatbots are particularly effective in scenarios where interactions are clearly structured and predictable, offering a quick and precise solution.

AI-Powered Chatbots
AI-powered chatbots rely on machine learning and natural language processing to continuously improve their responses. They can communicate with users in natural language and handle more complex, non-predefined queries. Their ability to learn dynamically allows them to adapt to user needs and continuously enhance the quality of their interactions.

Hybrid Chatbots
Hybrid chatbots combine both rule-based and AI-powered approaches to leverage the best of both worlds. They provide quick solutions for simple queries while also utilizing AI to handle complex requests that require human assistance. This flexibility makes them a versatile solution for a wide range of scenarios where both efficiency and problem-solving capabilities are needed.

Voice-Based Chatbots
Voice-based chatbots utilize speech recognition and text-to-speech technology to enable communication via voice. They are particularly useful in mobile or hands-free environments and are commonly found in devices such as smartphones, smart speakers, and even cars. Voice-based chatbots offer an intuitive and user-friendly way to interact, significantly simplifying the user experience.

Messaging Chatbots
Messaging chatbots are accessible via platforms such as Facebook Messenger, WhatsApp, or Slack, allowing users to communicate directly with a chatbot. They are ideal for quickly and efficiently delivering real-time information and seamlessly integrating into popular communication channels. This type of chatbot is particularly effective for message exchanges and tasks that require a fast response.

Intelligent Assistants
Intelligent assistants are specialized chatbots capable of performing a variety of tasks to personalize the user experience. They help organize daily activities, such as scheduling appointments, creating shopping lists, or finding information. They are often deeply integrated into systems and applications, providing seamless, efficient, and user-tailored support.

With these diverse chatbot types, businesses and users can leverage the technology across a broad range of contexts, from simple, standardized tasks to more complex and personalized interactions.

Artificial Intelligence (AI)

Thanks to advances in Artificial Intelligence, it has become very easy for humans to interact with automated systems or conversational interfaces such as chatbots or voice assistants. These systems are designed to communicate with users in a natural way and effectively and accurately process their requests. AI-based chatbots and voice assistants are continually being developed to better meet the needs and requirements of users.

According to MarketsAndMarkets, the global market for conversational AI is expected to grow from $6.8 billion in 2021 to $18.4 billion in 2026, representing a CAGR of 21.8%. This is due to the increasing adoption of conversational AI technologies in various industries, such as customer service, healthcare, and retail.

Furthermore, the use of chatbots has increased by 67% between 2018 and 2020. This increase is due to the growing automation of customer service and sales processes, where chatbots play an increasingly important role. Chatbots are also used in other areas such as recruitment and e-learning to improve efficiency and user experience.

Onlim has been active in the chatbot industry since 2015 and has successfully implemented several hundred chatbot projects for companies of various sizes and industries. Our years of experience allow us to present the best use cases and best practices so that you, too, can effectively leverage the benefits of this technology.

We are happy to share our experiences with you in this article: The Best Chatbot Use Cases from Various Industries.

Chatbots are a form of automated service, essentially a robot with which customers can communicate via a chat interface. They rely on Artificial Intelligence (AI), which operates through a set of defined rules and parameters.

Essentially, you engage in a virtual conversation with a robot that can perform certain tasks for you or answer your questions.

A chatbot functions similarly to a customer service representative. When a customer initiates a chat for assistance, the AI agent responds. For example, a customer might ask: “What time does your store open tomorrow morning?”

The bot then replies based on the information available to it: “Our store opens at 9 AM tomorrow and closes at 5 PM.”

Nowadays, chatbots can also handle complex queries such as:

“I placed an order last week, but it hasn’t arrived yet. Could you check the current status and let me know if there are any delays or if a reshipment is necessary?”

“I’m planning a trip to Japan in April and looking for cheap flights, hotels near Shinjuku, and recommendations for local attractions. Can you create a personalized itinerary with current prices?”

“My smartphone has been displaying an error message with code ‘XK-502’ since today. Can you explain what this error means, whether I can fix it myself, or if I should contact customer support?”

“I want to optimize my monthly expenses and need an analysis of my last three months of credit card transactions. Can you identify categories with potential savings and provide budgeting tips based on that?”

Have you ever asked Siri or Alexa for the weather forecast in your area? These are also a form of bots, specifically voice assistants, and they can provide responses that sound natural to humans.

To answer this question, we first need to divide chatbots into two categories.
As mentioned earlier, all chatbots are a form of artificial intelligence and complex programming. However, there are two main types. They differ fundamentally in one key aspect, which can be either artificial intelligence and machine learning or structured questions and answers.

Chatbots based on structured questions and answers are less complex than those leveraging machine learning to harness the full power of artificial intelligence. The visible chat interface appears very similar to users, making it difficult to distinguish between the two types.

These chatbots have a smaller knowledge base and limited capabilities.
In a rule-based chatbot, possible user inputs and corresponding responses are predefined. If a question is asked that was not previously defined, the chatbot will not be able to assist. They can provide correct output only for specific instructions, meaning the questions must align with the pre-set programming. Consider the following example:

The question “Will it rain tomorrow?” can be easily answered by this type of bot. However, if it is not programmed for it, a question like “Will I need a small umbrella tomorrow?” might cause confusion.

The bot would likely respond with: “Sorry, I did not understand the question.” It can only be as intelligent as the programming behind it. If you can only interact with a chatbot via buttons or a predefined menu and it struggles to understand you, you are probably dealing with this type of chatbot.

These chatbots are often implemented on messaging platforms for marketing purposes, as users do not necessarily need to interact with the bot extensively. They are well-suited for distributing newsletters or other content, generating leads, or conducting surveys.

Learning Through Machine Learning & Knowledge Graphs

If you are looking for a customer service chatbot for your business, this is the right type of automation for you. Unlike rule-based bots, chatbots based on machine learning can understand and process natural language. This is achieved through Natural Language Processing (NLP) and Natural Language Understanding (NLU). These technologies process natural language and do not require specific commands. A variation in the standard question will not confuse them, as they can automatically associate terms—for example, linking “umbrella” and “small umbrella.”

This means that machine learning-based chatbots become smarter with each interaction. However, the effort required to develop these chatbots is significantly greater. Now, let’s take a look at the role of knowledge graphs in this process.

Two very important factors for the overall performance of a chatbot are the structure and the quality of the data available to answer questions. This is where knowledge graphs come into play.

A knowledge graph is a synonym for a specific type of knowledge representation. It stores facts in the form of edges and nodes in a graph. Additionally, most knowledge graphs also store the schema of the data. Knowledge graphs unfold their full potential, especially in large and complex data structures. When used for chatbots, a knowledge graph offers two direct benefits: improved data integration and an enhanced conversation experience. New data sources can be integrated more easily, as they only need to be formatted and structured according to a specific schema. Knowledge graphs also provide greater flexibility in expanding existing knowledge.

It is also possible to link multiple knowledge graphs together. This allows for the creation of a modern, well-managed knowledge database for companies, which can, for example, be accessed via APIs and natural language interfaces.

Unfortunately, this question is not as easy to answer as “How much does a bottle of Fanta cost?” because the investment you make in a chatbot depends on various factors.

Things like the complexity of the bot, its AI capabilities, its architecture, technical integrations, infrastructure, support during and after launch, and more must be considered when estimating or comparing chatbot costs.

While you can sometimes start for free with very simple bot builders, chatbots built with more advanced bot builders often require setup fees ranging from €1,000 to €15,000+ and monthly fees between €100 and €5,000+.

We are happy to provide information about our products and pricing upon request.

This also depends on the factors that determine the cost of a chatbot.

At Onlim, we define and implement individual content together with customers, as well as all necessary interfaces to data sources. In addition, pre-configured modules are available through our Conversational AI platform.

After completing the technical setup, thorough testing and optimizations are carried out before the chatbot goes live. Customers can then manage all chatbot content, analytics, and more via our SaaS platform.