AI Knowledge Base for Machine Manufacturer
Development of an AI-powered knowledge database that captures, structures, and makes the expert knowledge of long-standing employees accessible to all staff via an intelligent chatbot.
AI Knowledge Base for Machine Manufacturer
Artificial Intelligence
The Challenge
For projects in this vein, our AI knowledge base service offering describes how we scope, build and support comparable deliveries from Germany.
A mid-sized machine manufacturer with 180 employees faced a critical problem: Within the next 5 years, 12 key employees with an average of 30+ years of operational experience would retire. This knowledge – from machine settings to troubleshooting to customer insights – existed only in the heads of these experts. Previous documentation attempts with SharePoint and Word documents had failed due to lack of usage and missing structure. The challenge: How can implicit expert knowledge be systematically captured and prepared so that new employees can quickly access it?
Our Solution
Solution screenshots
We developed an AI-powered knowledge database based on RAG (Retrieval Augmented Generation). In structured knowledge workshops, we captured the expert knowledge of experienced employees – through interviews, process observation, and documentation of decision paths. The content is stored in a vector database (pgvector) and made semantically searchable via GPT-4. Employees can ask natural language questions through a chat interface such as 'How do I set up the CNC mill for aluminum?' or 'What do I do when customer XY complains?'. The system finds relevant knowledge blocks and generates context-aware answers with source references. A feedback system enables continuous improvement of answer quality.
Results
After 6 months of use, measurable results are evident: The onboarding time for new employees was halved from 6 to 3 months. Recurring support requests to experts decreased by 65%. The system now contains over 2,400 knowledge blocks from 8 departments. User satisfaction is at 4.6 out of 5 stars. Particularly valuable: Even after the first 3 experts left, their knowledge remains fully preserved and accessible.
Features
Feature overview
- Natural language search via chat interface
- RAG technology for context-aware answers
- Source references and links to original documents
- Structured knowledge capture through workshop methodology
- Automatic categorization of new content
- Feedback system for quality improvement
- Role-based access rights (GDPR-compliant)
- Integration into existing intranet
- Offline-capable desktop application for workshop floor
- Multilingual (DE/EN) for international locations
- Automatic updates on process changes
- Anonymized usage statistics for knowledge gap analysis
Frequently asked questions about the AI knowledge base for mechanical engineering
When does a RAG knowledge base pay off for mechanical engineering companies?
It pays off as soon as service, sales, or commissioning teams repeatedly search manuals, maintenance guides, and spare-parts documentation. An AI knowledge base cuts lookup time, while AI projects show how technical knowledge can be turned into a productive system.
How does the system handle multiple manual versions and PDF sources?
Documents are versioned, semantically chunked, and enriched with metadata such as product line, build year, or component. This is where API and interface development enables context-aware answers instead of generic full-text hits, even when content comes from ERP, DMS, or service portals.
Can a mechanical engineering knowledge base reduce hallucinations?
Yes, if RAG is implemented with clear source citations, approval logic, and bounded answer context. Combined with software development and AI costs, the system can be designed so domain teams get traceable answers with explicit source references.
Which teams benefit most in mechanical engineering?
The biggest gains usually come in service, after-sales, technical documentation, and sales with complex product variants. Pairing an AI knowledge base with solid system integration creates consistent access to knowledge across silos.
Project Details
Client
Northern German Machine ManufacturerCompleted
2024
Technologies
Client Testimonial
"We used to answer the same questions 10-15 times daily. Now colleagues ask the chatbot first – and it usually knows the answer better than I do because it also has the knowledge of my colleagues. The project has taken away our fear of knowledge loss."
How Sandra finally stopped asking the same questions every day
Sandra has been in production planning for 8 months. She used to spend at least an hour every day looking for colleagues who could help her – often they were in the workshop or in a meeting. 'I constantly felt like I was bothering everyone,' she recalls. Since the knowledge database has been running, Sandra simply types her question into the chat. Most of the time she has the answer in under 30 seconds – including info on who originally contributed the knowledge. 'The best part is: I can look things up at 6 in the morning when nobody else is around yet. And I learn in the process because I see why something is done a certain way – not just how.' Her onboarding time was significantly shorter than for colleagues who started before her.
Team Voices
"Finally, I no longer have to explain 15 times a day how the special setting for the French plant works. The system explains it exactly as I would have – just more patiently."
Michael K.
Machine Operator, 28 years of experience
"When I heard that my knowledge was going 'into a computer,' I was skeptical. But the workshops were really good – they understood what we do, not just superficially. Now I'm proud when I see that my tips help others."
Petra S.
Quality Inspector, 25 years in the company
"The collaboration with Groenewold was different from other IT companies. They didn't just program but were genuinely interested in our work. You can tell from the result."
Jürgen H.
Workshop Manager
Partnership Instead of Project
- 1We took the time to truly understand the people and their work – not just check off requirements.
- 2Regular workshops with the experts where we came as listeners, not as know-it-alls.
- 3Even after go-live, we stay on board: Monthly check-ins, continuous improvement of answer quality, expansion to new departments.
- 4The client has a dedicated contact person who knows the system and the team – no anonymous ticket system.
More References
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