Case Study

Industrial Maintenance Copilot for SAP PM

Cuts SAP PM work-order creation from about 20 minutes to under 2 minutes with approval, improving maintenance capture across 5 factories.

Industry Industrial maintenance
Workflow SAP PM support
Focus Reviewable outputs

Challenge

Maintenance teams often work across fragmented histories: prior notifications, work orders, free-text notes, equipment context, and master data that all need to be checked before drafting the next action.

The operational problem is rarely just prediction. It is the repeated effort of finding the right historical context quickly enough to support triage, consistent record-keeping, and better work-ticket drafting inside an enterprise workflow.

Approach

We frame the system around the workflow first: what information the planner or engineer needs, which records are trustworthy, and where human review must stay in place.

  • Audit historical maintenance records, notification patterns, and equipment master data.
  • Identify reusable prior cases, failure language, and drafting patterns that help operators move faster.
  • Design retrieval and summarization steps around enterprise fields and operational review points.
  • Keep every suggestion traceable to the underlying record set instead of generating unsupported answers.

Solution

The copilot combines historical maintenance knowledge with structured asset context to support drafting and triage without taking control away from the maintenance team.

  • Retrieval-backed assistance over maintenance history, prior issues, and asset-specific context.
  • Workflow-aware drafting support for notifications, work tickets, and issue summaries.
  • Master-data grounding to keep terminology, asset references, and suggested actions aligned.
  • Reviewable outputs designed to fit enterprise approval and execution flows.

Results

Cuts SAP PM work-order creation from about 20 minutes to under 2 minutes with approval, helping teams record more minor maintenance events.

  • Cuts work-order creation time from about 20 minutes to under 2.
  • Keeps human approval inside the maintenance flow.
  • Encourages capture of minor events for future modeling.
  • Deployed across 5 factories.

Technology

Production-focused stack for retrieval, validation, API orchestration, and controlled deployment.

FastAPI Pydantic FAISS Docker Compose Python

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