MaintenanceAI's product methodology and maintenance/reliability framework were developed by Joshua Rivera. The system is informed by hands-on maintenance leadership, planning, CMMS, troubleshooting, root-cause, and reliability work in industrial environments.
AI is used as an analysis and development tool inside that human-directed framework. It does not replace customer engineering authority, OEM requirements, site safety rules, regulatory requirements, or qualified maintenance and reliability judgment.
The human methodology determines what MaintenanceAI looks for and how the product is bounded, including maintenance authority hierarchy, bad-actor focus, reactive-work signals, recurrence, backlog aging, PM technical basis, acceptance criteria, failure evidence, and release controls.
Project and customer requirements, approved technical documents, OEM information, engineering controls, and safety requirements take precedence over generic AI reasoning. Unknown technical values are identified for sourcing rather than invented.
Material product changes are supported by version history, human domain-method records, design decisions, testing, authorship records, and release attestations. These records preserve the human requirements, selections, modifications, corrections, and validation that shape the product.
MaintenanceAI can automatically generate analysis and recommendations from approved data. Those outputs apply a human-developed methodology, but an individual deliverable is not represented as personally reviewed by Joshua Rivera unless that specific review actually occurred.
Human methodology/product author: Joshua Rivera
Current methodology family: MaintenanceAI Human Methodology
\nMethodology identifier: MAI-HM-1.0
Operating principle: AI-assisted analysis under human-defined maintenance and reliability rules, with customer/OEM/site authority preserved.
This page intentionally describes the methodology at a high level and does not disclose proprietary scoring, prompt, orchestration, or implementation details.
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