Chatbots in customer service
Let your documentation do the talking
We help you make your company knowledge usable for a chatbot. We review your documents, knowledge articles, and technical documentation to assess their suitability for chatbot delivery and prepare them accordingly where needed. We also support you in defining requirements, advise you on selecting the right tools, and accompany you throughout the setup and ongoing operation of your chatbot.
The result is a solution that relieves your customer service team while providing users with accurate and helpful answers at all times.
Make your knowledge directly and intelligently usable
If you already have well-maintained technical documentation for your products or services as well as multiple knowledge bases, you are in a good position. With effective search functionality, your customers and your service and support teams can already find relevant information quickly and reliably.
Now you can extend this value by integrating a chatbot that answers product-related questions and supports users in solving problems. This reduces the workload on your support team and increases customer satisfaction. To ensure reliable performance, the chatbot should draw its answers directly from your technical documentation, knowledge bases, product data, and other authoritative sources. This ensures that customers and internal users always receive accurate, up-to-date, and product-specific information.
We support you in making your company knowledge usable for a chatbot. This is how we work.
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Chatbots in customer service
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Your chatbot with your data. This is how we work
Analyze documents. We analyze your documents to determine if they are suitable for a chatbot.
Prepare documents. We prepare documents, knowledge articles, technical documentation, and product information for chatbot delivery.
Develop content supply chain. We develop the content supply chain between information management systems and the chatbot. We help you choose the right tools for the supply chain.
Configure the chatbot. We create initial intents for the chatbot. Learn more about intents in chatbots in the FAQs.
Maintain the chatbot. We help you create additional intents and share our knowledge with your customer support team. If you wish, we can also provide long-term intent maintenance support.
Learn more about chatbots in customer service in the FAQs.
FAQs – Frequently asked questions about chatbots for technical documentation
How do chatbots work?
The term “chatbot” a contraction of the terms “to chat” and “robot”. Chatbots thus are robots which with one can chat. The procedure itself of how a chatbot works is rather simple: A user enters a text in natural language, which the computer then parses and translates into an action it can carry out, i.e., answering a question.
What’s the difference between a rule-based and an AI-based chatbot, and which is better suited for technical documentation?
In general, rule-based chatbots have possible responses pre-defined by the chatbot provider. Machine Learning-based chatbots calculate the response themselves based on trained AI language models. Rule-based chatbots are simpler as they follow pre-defined rules to respond to detected keywords. They work more accurately in small, more limited scenarios as the intents have to be defined manually. If the scope of a chatbot’s expertise is known, a rule-based chatbot makes more sense because the responses are pre-defined, meaning that the chatbot will only give users answers that have been deemed appropriate and correct by the chatbot provider.
Machine Learning-based chatbots are better-suited for scenarios in which the conversation flow cannot be clearly mapped or in which the scope is too extensive for a rule-based chatbot to manage. AI-supported chatbots work more flexibly due to their ability to ‘understand’ human language. As the responses are not pre-defined, AI-based chatbots are more suitable for creative tasks, such as writing assignments, than responses for which current, factual knowledge is required. They also achieve higher results in speech in long-term projects as it takes time to train them, but they then have an advantage in communication over rule-based chatbots as they sound more natural.
Although Machine Learning is often regarded as the more sophisticated and consequently better approach to chatbots, both Machine Learning and rule-based approaches are valid. Depending on circumstances such as time, finances and budgets, and complexity of the requirements, one approach might make more sense or be more suitable than the other, yet one cannot say one approach is generally superior to the other. What are the requirements for technical documentation to be accessible to a chatbot? The documentation has to be labeled in some form so that the chatbot knows which section of the documentation it should access. Depending on the complexity of the documentation, XML elements, or other simple forms of labelling can be enough; for extensive documentation, we recommend a metadata model so that the chatbot can, for example, also handle different variants of a product in its response. For processing in a chatbot, technical documentation should already be modularized, i.e. not be available as a large document. For example, the solution to an error message can be found more quickly if it is available as a text module and tagged with metadata such as error number and troubleshooting.
Where are chatbots already being used in technical documentation?
Mercedes-Benz AG provides an example of how technical documentation is linked to a chatbot. Mercedes Benz provides a virtual assistant with its Ask Mercedes chatbot. Ask Mercedes answers questions about the use of the vehicle, based on the information contained in the vehicle owner's manual. The chatbot is also available as an app and can be used via messenger services. Learn more.