FAQ: Chatbot for Companies

How does a chatbot-based knowledge management system help secure experiential knowledge?

The system makes experiential knowledge accessible across the organization by:

• Centralized knowledge base: The chatbot connects to a centralized repository that stores experiences, best practices, and solutions, enabling employees to access them at any time.
• Interactive knowledge retrieval: The chatbot supports natural-language queries, allowing employees to quickly obtain answers to specific questions and exchange experiential insights..
• Knowledge capture: When appropriately designed, the system can extract new knowledge from employee interactions and feed it back into the knowledge base, enabling continuous growth of organizational know-how.
• Training and onboarding: New employees can be systematically introduced to company knowledge and documented experience via the chatbot, reducing onboarding effort and ramp-up time.
• Accessibility: The chatbot is available 24/7, ensuring that knowledge can be accessed independently of time, location, or the availability of individual knowledge holders.

Can the system be integrated into existing IT environments?

Yes. A chatbot-enabled knowledge management system, such as the one developed at Fraunhofer IVV, can be integrated into existing IT infrastructures. The exact integration approach depends on your system architecture, technology stack, and requirements. These aspects are typically clarified in joint planning and consulting sessions to ensure a proper fit for your defined use cases.

Is the system cloud-based or can it be deployed on-premises?

The system can be operated either as a cloud-based solution or on-premises. Many industrial customers prefer on-premises deployment, as it ensures that data and organizational knowledge remain within the company environment. This setup allows full control over data governance while still enabling structured knowledge management.

Can employees easily contribute and update knowledge?

Yes. The system is designed to support continuous knowledge growth. Usability for both knowledge input and retrieval is a key design principle, enabling employees to contribute and maintain content with minimal friction.

How long does implementation take?

Implementation time depends on project scope and the maturity of the existing knowledge base. For example, developing a new knowledge delivery system on top of an existing knowledge base typically takes around three months. A more precise timeline and scope can be provided after a detailed requirements discussion.

As part of the WiBe-Back research project, an AI-supported knowledge management system was developed specifically for small and medium-sized bakeries. The solution combines a semantic wiki, a customizable ontology, an AI module for case-based problem solving, and a natural language chatbot.

This enables employees to access practice-relevant information via voice or text input, without the need for extensive pre-structured data entry. The system architecture is transferable to other industries and supports the stabilization of production processes, reduces skill gaps, and preserves the know-how of experienced employees over the long term.

As a result, SMEs become more resilient to skilled labor shortages, workforce turnover, and challenges arising from volatile raw material conditions.