Most industrial data never gets used, and the reason isn't volume — it's that no system holds the relationships between assets, sensors, work orders, procedures, and outcomes. IRIS Foundry's Cortex knowledge graph closes that gap by automatically discovering and maintaining the connections that exist in the plant but are recorded nowhere — what feeds what, what governs which procedure, what moves which KPI. Built on a standards-aligned namespace and grounding the Industrial LLM, Cortex turns disconnected historian tags, ERP records, P&IDs, and documents into a single, traceable source of operational context. The result is faster root-cause analysis, more reliable predictive maintenance, and AI answers that can be traced back to the tag, document, and work order they came from.
Key Takeaways:
- Industrial data problems are a context problem, not a volume problem — most data goes unused because no system links it to the rest of the plant.
- Cortex is an open, labeled entity graph that connects assets, sensors, procedures, and events as machine-readable relationships, not just centralized storage.
- Entity matching reconciles the same physical asset across systems (historian, ERP, P&ID) automatically, replacing a manual reconciliation process that used to take hours per investigation.
- The Industrial LLM grounds every answer in the graph, reducing hallucination and making each response traceable to its source.
- P&ID ingestion turns static drawings into interactive, queryable diagrams, giving engineers a live entry point into connected equipment, health scores, and open work orders.
- Customers report 30–50% reductions in unplanned downtime and 3–6x ROI in year one from reliability-led programs built on the graph.
- The graph compounds in value over time: every asset, procedure, and failure mode added makes the next question cheaper to answer than the last.
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