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The search for relevance
Human engineers rarely assess industrial data in isolation:
- They see a pressure spike and recall the maintenance history
- They hear an alarm and connect that piece of equipment to others that depend on it
- They read a specification document and judge its veracity against the vendor’s track record.
The human engineer instinctively distinguishes relevant from irrelevant context – it’s bound up in training and experience.
And it’s why many industrial AI initiatives struggle to move beyond narrow use cases.
Ask any industrial player and they will admit that most institutional knowledge is uncodified, often intuitive and context-based – and it can’t be interrogated by even the best AI.
AI is powerful at analysing patterns and information, but without operational context, it struggles to determine relevance, causality, importance or the operational consequences of incoming data.
In industrial environments, the challenge is rarely the absence of data. It is the absence of connected understanding.
This is the gap knowledge graphs are designed to address.
Layering in context
Knowledge graphs help organisations codify operational context, rather than leaving it fragmented across systems or dependent on individuals.
They act as a living map of industrial relationships – between assets, tags, alarms, calculations, documents, barriers, maintenance history, process conditions – inside systems, processes and infrastructure.
Every piece of data is connected, and its relationship is defined inside a relationship model.
Take a pressure safety valve: in a traditional system, it exists as a tag – a number in a historian. But in a knowledge graph, it is connected to the barrier it supports, its operating envelope, its last three maintenance records and the status that changes when it opens. Change one element and the knowledge graph tells the system what else is affected.
Knowledge graphs help AI Large Language Models (LLM) understand not just what data exists, but how operational elements relate to one another. They provide the operational context AI/LLM systems have historically lacked.
Knowledge graphs model the relationships between operational elements directly, cutting through organisational and system siloes to provide AI with a far richer contextual foundation.
Traditional RAG has been highly effective at helping LLMs retrieve relevant information. But as Neo4j explains, industrial decisions rarely depend on isolated facts alone. They depend on relationships, dependencies and operational context spread across multiple systems and datasets.
Graph-RAG extends this approach by grounding AI in a connected operational model, allowing it to reason across assets, events, process conditions and dependencies rather than disconnected documents or data points.
Research from Google and others has reinforced the importance of strong contextual grounding in improving AI retrieval and reasoning. In industrial environments, that means helping engineers navigate operational complexity with greater speed, confidence and traceability.
Knowledge graph powered AI in action
We tried this concept in an internal R&D project designed interpret information across multiple disconnected systems, including barrier status data, work orders and FMECA (Failure Mode, Effects, and Criticality Analysis) analyses to assess the safety status of the plant with the help of an LLM chatbot.
Rather than building a generic AI search assistant, Eigen developed a Graph-RAG AI chatbot grounded in an underlying knowledge graph representing the plant’s operational relationships.
The system we built connects safety barriers, live status data, work orders, maintenance history, FMECA analyses, operational standards – all inside a structured operational model.
Now, connected to real customer-data, the chatbot can answer questions such as:
- Which work orders should be prioritised to maximise barrier integrity?
- What actions are recommended based on failure analyses?
- List the areas with status, impairments, work orders etc.
In addition, the system provides traceable answers grounded in connected operational data rather than simply surfacing related documents.
By combining Graph-RAG with a controlled retrieval architecture, the system has improved the reliability and contextual relevance of responses.
The result is not simply a better AI chatbot, but a more effective way for engineers to navigate operational complexity and make faster, more confident safety decisions.
Conclusion
The organisations that will gain the most from industrial AI aren’t necessarily the ones with the biggest models or the most data. They are the ones that successfully connect operational relationships, preserve engineering understanding and give AI the context it needs to reason reliably.
Knowledge graphs provide that operational context layer.
If you would like to explore what this could look like in your environment, get in touch with Joel Chacon, or check out the complete details in the paper published in “IChemE Digital Chemical Engineering”: “Enhancing Process Safety with Graph-RAG Based Decision Support AI chatbot A Digital Companion for Safety-Critical Environments”.
