Any AI can draft an FMEA.
Keeping 500 of them true takes a system.

Where general-purpose AI falls short

Same question, fundamentally different architecture.

	ChatGPT / Claude / Copilot
Memory	✕ Remembers conversations and projects. Doesn’t keep a governed, versioned record of every FMEA row, its source and who approved it.	✓ Persistent industrial knowledge base. Failure modes, asset hierarchies, and risk rankings stored permanently.
Feedback loop	✕ A correction may be remembered in one chat. It isn’t applied to every related row on every related asset, and nobody can audit it.	✓ Engineer corrections feed back permanently. Every edit improves the next draft for that asset and every related asset.
Data integration	✕ Connectors can read a system. They don’t continuously match new failure reports to FMEA rows across sites.	✓ Ingests failure reports, manuals, BOMs and PDFs continuously. Connects to maintenance, asset and document systems.
Traceability	✕ Can cite the file behind an answer. Doesn’t keep row-level provenance with a revision history an auditor can follow.	✓ Every FMEA row linked to source paragraph, page, and document. Full audit trail with revision tracking.
Knowledge retention	✕ Trained on public internet data. Knows what FMEA is, not what fails at your plant.	✓ Built from your data, your systems, your history. Knowledge compounds across your sites.
Multi-agent coordination	✕ Can run agents on a task you set up. Not a standing team of agents that maintains your FMEAs, with shared memory and engineer review.	✓ Specialized agents for data quality, failure extraction, hierarchy building, and action recommendation. Shared memory.
Standards compliance	Generic awareness of FMEA concepts. No structured alignment to IEC 60812, AIAG-VDA, or SAE J1739.	✓ Structured around IEC 60812 and common frameworks including AIAG-VDA and SAE J1739.
Output format	Exports a spreadsheet on request. Each export is a new file, disconnected from the last one and from its sources.	✓ Structured, searchable, exportable. Linked to source documents. Updatable when data changes.
Where it runs	The vendor’s app. Your FMEA program lives in chat history.	✓ Your cloud, your models. Swap model providers and the record stays. Full export at any time.
Adaptation over time	✕ Models improve every few months, for everyone. They don’t learn from your engineers’ decisions on your assets.	✓ Every correction and new data source makes your system smarter, on every related asset.

It’s not Tacit AI vs ChatGPT.
It’s static vs living.

Every FMEA tool treats the FMEA as a document you fill out and file away. We treat it as a system that knows when it’s wrong.

	Static FMEA tools
After approval	✕ Sits in Teamcenter, Windchill, or Excel. Nobody touches it until the next design revision.	✓ Failure reports match to FMEA rows continuously. The FMEA tells you when it needs attention.
Field failure occurs	✕ Quality engineer opens a CAPA. Nobody updates the FMEA. Same failure mode recurs for years.	✓ Failure matched to FMEA rows. Failed controls flagged as regressions. Control plan marked ineffective.
New failure mode appears	✕ Never in the FMEA. Nobody adds it. Gap is invisible until the next recall or audit finding.	✓ System proposes a new row with component, failure mode, cause, effect, and risk scores. Engineer accepts or dismisses.
Engineer leaves	✕ 30 years of knowledge walks out the door. Replacement opens 50 spreadsheets with no context.	✓ Every row has source citations. Every correction stored. Every risk ranking traces to evidence.
Occurrence rates shift	✕ Original occurrence rating from 3 years ago. Nobody recalculates.	✓ Weibull parameters recomputed from time-to-failure data. Occurrence ratings update with evidence.

APIS IQ, Relyence, Teamcenter FMEA, Windchill Quality, and Excel are all in the left column. Not because they’re bad tools. Because they were designed to create a document, not to keep it alive.