BKOAI converted PI tag mapping into a structured reasoning workflow and stored the decision path in a context graph, creating reusable decision memory with improved accuracy and traceability
Goals
Improve PI tag-to-equipment mapping accuracy and consistency.
Turn mapping from a one-time lookup into a reusable contextualization workflow.
Preserve decision logic for future reuse and governance.
Challenges
PI tags are often inconsistent, abbreviated, or incomplete.
A single tag may match multiple equipment candidates.
Traditional methods preserve the final result but lose the rationale, alternatives, constraints, and validation history.
Solutions
Use Contextualization Agents to expand tag metadata and compare equipment candidates.
Apply process context, equipment hierarchy, and operating constraints.
Embed self-reflection and human verification into the workflow.
Store the complete decision history in a structured Context Graph.
Results
Reuse prior mapping decisions instead of restarting inference.
Reduce hallucination risk and manual rework.
Improve consistency, traceability, and auditability.
Scale contextualization across assets and deployments.
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