Agentic AI Asset Performance Management for Power Transformers
Unify transformer evidence, reason over risk, and guide verified maintenance decisions with standards-aware AI agents.
GridAPM Platform
From transformer data to verified decisions
GridAPM connects diagnostic evidence, engineering rules, fleet history, and human review into one repeatable reliability workflow for critical transformer assets.
1
Ingest
Collect data from sensors, lab tests, inspections, and enterprise systems.
2
Correlate
Align multi-source evidence and fleet context to each asset digital record.
3
Reason
Agentic AI evaluates conditions, trends, uncertainty, and physics-based constraints.
4
Verify
Engineers review recommendations with confidence notes and full traceability.
5
Recommend
Actions are prioritized by risk, confidence, consequence, and implementation guidance.
6
Report
Audit-ready reports include evidence packs, rationale, signoff, and decision logs.
Status key Normal Within normal range Monitor Monitor condition Action Action required Approved Engineer signoff complete
All evidence and decisions are traceable and time-stamped for audit readiness.
Reliability program
Transformer reliability, built as an engineering workflow
The platform is intentionally practical: evidence in, transparent reasoning out, and engineer-approved actions captured for maintenance planning and audit readiness.
Transformer evidence layer
Unify diagnostic files, monitoring signals, inspection notes, and work-order context into one asset model.
Agentic reliability workflow
Move from ingestion to reasoning, verification, recommendation, and reporting with bounded AI tasks.
Fleet risk prioritization
Rank assets by transparent evidence so teams can focus engineering review where the consequence is highest.
Human-in-the-loop governance
Keep accountability with engineers through review states, signoff, confidence notes, and traceable assumptions.
Audit-ready reporting
Create review packages for internal reliability boards, maintenance planning, and external engineering discussions.
Offline-first pilot path
Support sensitive utility and industrial environments with a workstation-oriented evaluation model.
Technical foundation
Standards-aware AI with engineering judgment in control
GridAPM does not turn transformer diagnostics into a black box. The product direction is anchored in recognized diagnostic practices, transparent uncertainty, and accountable human review.
Public source links are provided for context. GridAPM paraphrases guidance and does not reproduce copyrighted standards text.
IEEE C57.104 Dissolved gas analysis interpretation for oil-filled power transformers. IEC 60599 Guidance for interpreting dissolved and free gases in mineral-oil electrical equipment. IEC 60270 Partial discharge measurement discipline, calibration, and interference context. IEEE C57.149 Frequency response analysis procedures, interpretation, and long-term record keeping. CIGRE TICM Transformer intelligent condition monitoring and integrated diagnostic systems. NIST AI RMF Trustworthy AI risk management, explainability, and governance principles.
Research for transformer reliability leaders
Technical writing for asset managers, transformer engineers, and reliability teams
Product Demo
GridAPM Demo: TransformerAPM Workbench
A guided product demo of TransformerAPM, the first GridAPM workbench for transformer diagnostics, AI-assisted reasoning, health indexing, and engineering reporting.
Agentic AI APM
Agentic AI APM Software for Power Transformers
A practical architecture for agentic AI asset performance management software that helps transformer teams move from diagnostic evidence to human-reviewed maintenance decisions.
Dissolved Gas Analysis
DGA Trend Analysis Beyond Single-Snapshot Interpretation
How multi-gas trends, operating context, uncertainty, and human-reviewed AI workflows can improve dissolved gas analysis for power transformer reliability.
Start a transformer reliability pilot
Share your fleet profile and diagnostic workflow.
GridAPM will propose a focused evaluation path for your engineering team, including the evidence streams, review steps, and reporting outputs that matter for your first pilot.
Human-reviewed recommendations
Diagnostic evidence model
Audit-ready report workflow