Chatbot in customer service: direct access to all your service knowledge
AI-based chats that are based on structured knowledge and finally provide your customer service with reliable answers.
- AI answers you can rely on
- Consistent responses across all channels
- Secure access to company-wide knowledge
Leading companies trust us
Chat with your company knowledge for outstanding customer support
AI chatbots relieve the burden on support teams and speed up responses, but only if they are truly reliable. Many solutions fail due to unstructured data and deliver inaccurate results.
Our solution: Structured content and knowledge graphs provide precise, context-based answers.
- Less effort for support teams
- Better customer experiences through reliable AI responses
From daily service problems to noticeable relief in customer service
This slows down your service teams:
- Distributed knowledge
Information is stored in PDFs, tickets and various systems - Lack of context
Product configuration, history and use case are often missing in the diagnosis and response - High team effort
Documentation and training of new employees take a lot of time
Advantages with AI-based chats:
- Immediately in context
Relevant repair, maintenance and troubleshooting information without lengthy searches - Precise answers
Answers take into account product configuration, history and use case - Less effort in the team
Fewer escalations, less documentation work, faster onboarding
40% better answers through real context
Current studies show that AI models deliver significantly more precise results when they are based on structured knowledge: While direct queries on databases only achieve a low hit rate, accuracy increases significantly with a knowledge graph as a basis.
The additional context makes the difference - for comprehensible and reliable answers.
Take the pressure off your support team with a chat assistant that really helps.
Our approach
Step by step
to reliable AI service chat
Start with initial use cases and build your intelligent service assistant step by step. From the idea to technical validation and a scalable solution, this creates a sustainable basis for your reliable AI service chat.
01 Consulting
Together, we analyze your initial situation and identify specific use cases in your service department. In doing so, we focus on rapid added value and a clear objective for an effective, intelligent chat assistant.
02 Proof of concept
In the PoC, we show the technical feasibility and demonstrate how your data can be transformed into networked knowledge. We analyze your existing knowledge sources (tickets, documents, systems, empirical knowledge) and structure them in a knowledge graph. This creates a consistent knowledge base that AI can access contextually.
03 MVP & Scale-Up
The first productive use case is created with a minimum viable product (MVP). A pilot with real service cases quickly demonstrates the added value in day-to-day support. Building on this, we integrate the AI chat into your existing service systems and roll out the solution step by step.
Service Copilot from ZEISS RMS
The intelligent assistant that every service team wants.
For the service team at ZEISS Research Microscopy Solutions (RMS), every request meant having to compile information from countless documents in far too many sources. Now they are supported by Service Copilot, an intelligent assistant that links knowledge graphs with Agentic AI – and makes the service for ZEISS microscopes as efficient as research demands.
Knowledge graphs: the basis for reliable AI
Our chat assistant is not based on probabilities, but on networked knowledge. We use knowledge graphs to structure product data, documentation, processes and guidelines, making them optimally usable for AI systems.
RAG or GraphRAG: Which knowledge base provides reliable AI answers?
Retrieval Augmented Generation (RAG) is the first step for many companies to link LLMs with their own information. However, classic RAG quickly reaches its limits when it comes to complex, connected company knowledge.
GraphRAG combines RAG with knowledge graphs to create significantly more context, consistency and control.
Classic RAG finds information
Documents are converted into vectors, semantically searched and passed to an LLM as context.
- No explicit understanding of relationships and context
- Inconsistent answers to similar questions
- Hallucinations possible despite retrieval
- Low comprehensibility of the answers
- Limited suitability for complex corporate knowledge
GraphRAG understands connections
A knowledge graph explicitly models entities, relationships and rules. RAG accesses this knowledge in a targeted manner.
- Significantly higher contextual reference
- Consistent AI responses across different queries
- Fewer hallucinations due to explicit knowledge models
- Explainable & comprehensible answers: Sources, relationships and decision-making logic are transparent
- Stable foundation for scalable AI applications
Contact person
We look forward to your inquiry
Dr. Stefan Bradenbrink