Why not just use ChatGPT?

General AI forgets your plant.
Tacit AI learns it.

ChatGPT, Claude, and Copilot can generate FMEA-shaped text from an uploaded manual. But a reliability program needs persistent memory, engineer feedback loops, audit trails, and knowledge that compounds across sites. That requires a system, not a chatbot.

Side-by-side

Where general-purpose AI falls short

Same question, fundamentally different architecture.

	ChatGPT / Claude / Copilot
Memory	✕ Every session starts from zero. No memory of your equipment, failure history, or previous analyses.	✓ Persistent industrial knowledge base. Failure modes, asset hierarchies, and risk rankings stored permanently.
Feedback loop	✕ Correct an output, the correction vanishes next session. Same mistakes repeated indefinitely.	✓ Engineer corrections feed back permanently. Every edit improves the next draft for that asset and every related asset.
Data integration	✕ Upload one file at a time. Cannot connect to SAP PM, Maximo, or your EAM behind a firewall.	✓ Ingests work orders, manuals, BOMs, PDFs continuously. Connects to CMMS, EAM, and DMS systems.
Traceability	✕ No source linking. No per-cell provenance. Cannot tell you which manual page produced which failure mode.	✓ 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 deployments.
Multi-agent coordination	✕ Single model, single conversation, single task. No specialization.	✓ 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	Prose or markdown table. Copy-paste into spreadsheet manually.	✓ Structured, searchable, exportable. Linked to source documents. Updatable when data changes.
Security	Data leaves your network. Shared infrastructure. Potential training on your inputs.	✓ Private deployment. Your data stays within your perimeter. No model training on your data.
Adaptation over time	✕ Static model. Same capability today as 6 months ago (for your use case).	✓ Every deployment, correction, and new data source makes the system smarter. Knowledge compounds.

A chatbot gives you text.
A system gives you a program.

ChatGPT is a brain without memory. Tacit AI is a brain with your plant’s entire operational history.

It remembers.

Every manual, work order, and prior FMEA stored permanently. Every correction propagates to related assets. A failure pattern at one site lifts drafts at every other.

It’s traceable.

Every FMEA row links to the source paragraph, page, and document that produced it. Auditors verify. Regulators trace. No row exists without provenance.

It’s private.

Your data stays in your network. No shared infrastructure. No model training on your inputs. Full data export at any time. Your data is yours.

The real comparison

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.	✓ Work orders 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.

The gap widens every month.

Industry-specific models, agentic learning, and engineer feedback loops. Every deployment makes the next one better.

At 60% accuracy, engineers rewrite every row. At 90%+, they confirm and move on. Every correction narrows the range and lifts the next draft, for every team, on every system.

The real test

Ask ChatGPT to write an FMEA.

Generic failure modes from training data vs. your data, your history, and your standards.

ChatGPT

Hallucinated sources. No traceability. Same output on day 1 and day 365.

Tacit AI

Every row traced to source. Every correction retained.

One is a guess. The other is evidence.

See the difference on your data

Send us a document. In 30 minutes we run it through the pipeline and show you what we find versus what ChatGPT produces. No commitment.

Book a working session

General AI forgets your plant.Tacit AI learns it.