Siemens Energy - Marine World

Industry:Energy

A requirement engineering workshop was held together with Siemens Energy. The aim of the workshop was to develop a common understanding of the new Siemens Energy Marine World in order to subsequently work out the different user roles and their requirements for the product. The outcome formed the basis for the requirements for the system to be selected.

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Smart knowledge transfer

The chatbot enables specialist users to find complex information immediately - without manual searching or lengthy reading. This turns knowledge into an active tool.

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Time savings for editors and users

Automated analysis and intelligent queries save hours of research work every day. Editors and administrators can concentrate on content instead of searching.

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Future-proof knowledge infrastructure

The architecture of the chatbot is scalable, GDPR-compliant and expandable. New data sources can be easily integrated - the solution grows with demand.

Siemens-Case-1

Challenges

The technical challenges

The aim was to develop an AI-supported search solution that captures the content of thousands of specialist articles, PDFs and studies, links them semantically and makes them accessible in a dialog-based manner - without changing existing systems.

  • Processing of unstructured PDF documents and HTML content
  • Development of a high-performance vector database for semantic searches
  • Integration of an LLM for dynamic response generation
  • Secure hosting in a GDPR-compliant cloud environment
Realization

The implementation steps

The project involved analysing and structuring the existing information archive and optimizing it for AI applications. P&M supported the entire process, from data preparation to productive integration.

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Data analysis & target definition

Together with the editorial team, the existing content landscape was examined and prioritized. P&M defined the relevant sources, subject areas and interfaces for the AI-based search. This created a clear basis for structure, data quality and subsequent scaling.

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Normalization & preparation

Unstructured PDFs and articles were automatically extracted, cleansed and standardized. This data preparation created a consistent knowledge base that can be semantically analyzed and provides the chatbot with reliable information.

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Vectorization of the database

A Weaviate vector database was created on the basis of the cleansed content. It links information semantically and enables the system to recognize connections between topics - regardless of file format or text structure.
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Integration of RAG & LLM

P&M then linked the vector database to a large language model (OpenAI GPT) using a RAG architecture. This resulted in an adaptive system that responds to complex questions in natural language and obtains comprehensible sources.
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Frontend development & testing

The chat interface was designed to be modern, accessible and intuitive. A multi-stage test process ensured performance, relevance and response quality - with a focus on user-friendliness and data protection.
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Go-Live & further development

After going live, P&M provides ongoing support - with monitoring, regular updates and continuous optimization of response quality through feedback loops.

Result

A future-proof modern platform

The result: an intelligent, editorially validated chatbot that makes all of Kommune21's expertise immediately accessible - context-sensitive, dialog-oriented and secure.
  • Relevant results in seconds instead of minutes
  • Seamless integration into the existing website
  • 100% GDPR-compliant processing
  • High scalability for growing content volumes
Siemens Case 2