Knowledge Graph + AI for Industrial Asset Intelligence
Short answer. Combining a knowledge graph with AI gives industrial asset intelligence both context and prediction. The graph links each asset to its components, sensors, maintenance history, documents and procedures; AI models predict condition and risk; and GenAI answers questions over both with citations.
Knowledge Graphs
Part of the guide: Knowledge Graphs for Oil & Gas
What problem does this solve?
Asset data is split across EAM, historians, drawings and documents. Predictive models lack context, and engineers cannot ask cross-cutting questions.
What does the architecture look like?
How does it work?
Graph as context layer
Hierarchies, connections and history live in the graph, so a model can consider an asset's siblings, upstream equipment and past failures.
Prediction on the graph
Risk scores are written back to asset nodes, letting planners query risk by line, site or asset class.
Questions in plain language
GraphRAG retrieves the relevant sub-graph and documents so answers cite specific assets and records.
Implementation considerations
- Align the asset hierarchy with your EAM before building.
- Keep the graph synchronised through change data capture.
Frequently asked questions
Do we need to replace our EAM?
No. The graph references EAM records and keeps them synchronised.
Related solutions
Knowledge Graphs
A knowledge graph connects assets, locations, events, sensors, models, procedures and outcomes as explicit relationships.
SolutionPredictive Maintenance
AI-based predictive maintenance estimates the probability and timing of equipment failure from sensor data, operating conditions and maintenance history, so work is planned before a breakdown.
SolutionEnterprise AI
Enterprise AI at AgenticNex AI means engineering AI systems that run inside your operations: connected to your data, governed, monitored and owned by your teams.
See Related Case Study
Talk it through with an engineer who builds these systems.
See Related Case Study