Knowledge graphs as the basis for reliable AI in the company
Link data and information, build an AI-enabled knowledge base and increase efficiency and transparency in your company.
- Structured company information instead of distributed data
- Networked knowledge across systems and domains
- Reliable AI answers instead of hallucinations
Leading companies trust us
Whitepaper
The path to the knowledge graph
Using the example of a pump manufacturer, find out how product data is transferred into a knowledge graph and what potential this offers for data-driven applications.
From data silos to Ki-capable knowledge
Typical challenges
- Distributed and unstructured information
Company information is available in many systems and cannot be used as a structured, networked knowledge base. - AI without a reliable knowledge base
Without knowledge models and knowledge networking, AI systems do not provide consistent or reliable answers. - Lack of transparency of relationships
Important relationships between data, documents and entities remain implicit and only exist in the minds of employees. - High integration and scaling costs
The development of an enterprise knowledge graph often fails due to complex IT landscapes and a lack of scalability.
Added value through knowledge graphs
- Structured company information
Knowledge graphs link information across systems and create a central, consistent knowledge base. - AI-enabled knowledge base
As the basis for Knowledge Graph RAG and Knowledge Graph LLM, knowledge graphs enable context-related and reliable AI answers. - Explicit knowledge models
Relationships and correlations are modeled and made machine-readable – ideal for complex industrial and mechanical engineering scenarios. - Basis for AI applications
An enterprise knowledge graph forms the heart of portals, assistance systems, dashboards and other data- and AI-driven applications.
Topix
Knowledge Graph briefly explained
What are Knowledge Graphs
Knowledge graphs: the basis for reliable AI
Knowledge graphs link information from different systems into explicit, structured knowledge, make hidden connections visible and create the basis for better decisions and AI applications.
PANTOPIX is your experienced partner for the design, modeling and implementation of powerful knowledge graphs in existing IT architectures.
Knowledge Graphs & LLM
Why knowledge graphs make AI reliable
Knowledge graphs and LLMs complement each other perfectly: while LLMs understand and generate language flexibly, knowledge graphs provide the structured, reliable knowledge base in the background. This results in more precise, comprehensible and context-related answers.
Our approach
Step by step
to the knowledge graph
Start with initial use cases and expand the knowledge graph step by step.
From the idea to technical validation to a scalable solution, this creates a sustainable basis for AI applications.
Consulting
Together, we analyze your initial situation and identify specific use cases. We analyze your data landscape, existing information flows and data silos. In doing so, we focus on rapid added value and a clear objective for the use of a knowledge graph.
Proof of concept
In the proof of concept, we show the technical feasibility and demonstrate how your data can be transformed into networked knowledge. We develop a robust knowledge model (taxonomy, ontology, thesaurus) and a technical architecture, the basis for a scalable, AI-capable knowledge base.
MVP & Scale-Up
The first productive use case is created with a minimum viable product (MVP). Building on this, we create a roadmap for the rollout and company-wide integration so that your knowledge graph is successful and AI-ready in the long term.
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.
"We have been using knowledge graphs with our customers for a long time to link information from different sources. One current application scenario is AI chatbots, where hallucinations and false information pose a major risk. This risk can be reduced by validating answers using knowledge graphs."
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.
Up to 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.
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
Karsten Schrempp
FAQ
Frequently asked questions about knowledge graphs and AI in the company
What is a knowledge graph?
A knowledge graph is a structured representation of knowledge in the form of a semantic network. This connects data via nodes (entities) and edges (relationships) and uses semantics to make the connections between information understandable. This breaks down information silos and makes company-specific knowledge – for example about products – usable. It does not matter whether the data is available in structured or unstructured form.
What are AI knowledge graphs?
AI knowledge graph is based on the principle of the knowledge graph, but goes one step further: AI techniques are used to automatically create, expand or evaluate the graph.
- AI helps, for example, with extracting knowledge from texts, recognizing missing relationships or deducing new facts.
- Focus: Integration of AI to not only store knowledge, but to make it intelligently usable.
Why do we need knowledge graphs at all?
Knowledge graphs help to connect information from different data silos and bring it into a common context. This makes connections visible that were previously hidden. Knowledge becomes structured, findable and reusable. A well-known example is Google’s Knowledge Graph: it ensures that Google not only recognizes search terms, but also understands the meaning behind them, e.g. that “Paris” can be a city, a person or a figure from mythology. It is precisely this principle that can also be used in companies to intelligently link technical information, product data or customer knowledge and make it accessible. Such knowledge presentations are particularly valuable in the age of AI, as they provide fact-based data structures and thus reduce the risk of so-called “hallucinations”. They make knowledge transparent and verifiable and create a reliable basis for data-driven decisions, analyses and new services.
What is the difference between RAG and GraphRAG?
RAG (Retrieval-Augmented Generation) searches documents and provides AI answers based on texts. This works well for simple queries – but classic RAG quickly reaches its limits when it comes to complex, highly networked knowledge: Answers can be inconsistent, hallucinations can occur and comprehensibility suffers.
GraphRAG combines RAG with a knowledge graph that explicitly models entities, relationships and rules. This allows companies to benefit from:
- Better context and consistent answers
- Less hallucinations through structured knowledge
- Explainable results with transparent sources and correlations
- Scalable solutions for complex corporate knowledge
In short: Classic RAG provides quick answers from documents, GraphRAG provides reliable, comprehensible and consistent answers, especially for complex, networked knowledge.
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