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FMEA AI Agents to generate better FMEA's and Control Plans.

“AI agents that reuse your Foundation FMEAs, generate context-aware DFMEAs and PFMEAs, and validate every entry against AIAG-VDA, AIAG 4th Edition, and SAE J1739 - through conversation, not a blank spreadsheet.”

The FMEA platform since the 1980s · 670,000+ users · Built by the team that helped write the FMEA standard.

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The Problem

FMEA quality depends on whoever's in the room - and the room is short on senior reviewers. Every new product or process still starts from a blank page, even when much of the content already exists somewhere in a prior FMEA. Inconsistent severity ratings, missed multi-point faults (20–30% of electronics failures, per Omnex), and incomplete entries surface late - usually in a customer audit, not before it.

The fix isn't more headcount. It's an FMEA system that already knows what your organization knows.

The Solution

AQuA Pro now includes the first agentic AI FMEA agent on the market - built on AQuA Pro's Foundation FMEA and reuse architecture, not a generic chatbot bolted onto a spreadsheet.

Generative AI (ChatGPT, Copilot) creates content from a prompt. Agentic AI goes further: it's autonomous, goal-driven, and able to take multi-step action - reading your existing FMEAs, asking clarifying questions, and producing a structured, standards-compliant document, not just a paragraph of suggestions.

Evolution Stages of AI

Core Capabilities

What the agent does inside AQuA Pro

Reuse Foundation FMEAS

Pulls from your Global, Family, and Product-level FMEAs so new documents inherit what's already validated.

Recommend Failure Modes

A Causes & Controls & Actions

Grounded in your data first, with optional access to approved external sources.

Validate Automatically

200+ machine-learning rules check completeness, consistency, and standards compliance before a human review.

Chat-Based Development

Build, edit, and re-rate an FMEA entirely in natural language, then push it into AQUA Pro on command.

How It Works

A five-step flow, written for skimmability - pair with the embedded demo clips from the webinar (DFMEA generation; PFMEA generation from existing process segments):

  • Tell the agent what you're building - a product, a process, or a part number you already have in AQuA Pro.
  • The agent pulls context - from your Foundation FMEAs, Design/Process Reuse Libraries, and (if enabled) approved external sources.
  • It drafts the FMEA - failure modes, effects, causes, controls, in AIAG-VDA or AIAG 4th Edition format, your choice.
  • You refine it in chat - add a failure mode, override a severity rating, ask it to regenerate causes for a specific effect.
  • Send it to AQuA Pro - one confirmation step, and the FMEA lands in your system, auto-populating ~90% of the Process Flow and 60–70% of the Control Plan.

From request to a validated FMEA inside AQuA Pro - five steps

AI-Powered Validation

Generation is half the story. The same agent checks AIAG-VDA DFMEA, AIAG 4th Edition DFMEA, Process Flow, AIAG-VDA PFMEA, AIAG 4th Edition PFMEA, and Control Plan documents against 200+ pre-programmed machine-learning rules - configurable to your organization's own best practices. A sample review in the webinar surfaced 29 findings, from missing header data to severity/regulatory mismatches, before the document ever reached a human reviewer.

Why the AI Output Is Trustworthy

Generic AI tools hallucinate because they have nothing to ground their answers in. AQuA Pro's agent doesn't have that problem - it inherits from a reuse architecture Omnex built in the 1980s and has refined for 40+ years: Global, Family, and Product-level FMEAs that cascade automatically, plus Design and Process Reuse Libraries for proven components, tests, and operations. Update one parent FMEA and every child FMEA worldwide updates with it - and the AI agent reasons over that same structure instead of starting cold.

Foundation FMEA Reuse Hierarchy

Update one parent FMEA, and every child FMEA Inherits It

Data Security & Human-in-the-Loop

Your data trains the agent through supervised learning and stays inside your network and firewall - it is not used to train a public model. External sources are optional and can be switched off entirely. Hallucination risk is managed through retrieval-augmented generation and fine-tuning, and the platform is LLM-agnostic (demoed on an OpenAI framework, swappable per your IT strategy). Every AI-generated FMEA is reviewed by a human before it's finalized - the agent accelerates the work, it doesn't replace the sign-off.

Standards Supported

AIAG-VDA Handbook (1st Edition), AIAG Core Tools (4th Edition), and SAE J1739 - including multi-point fault analysis and DFMEA-FMEDA-FTA linkage. Medical device teams can build on AIAG or SAE-based FMEA approaches (ISO 14971 sets requirements but doesn't define FMEA mechanics, so Omnex teaches and configures to AIAG/SAE structure for medical device customers). Functional safety customers can extend into the e-mobility/ISO 26262 suite.

Benefits

  • Faster development No blank-page start; every FMEA inherits from what your organization already proved out.
  • Consistency at scale Global/Family inheritance means one correction updates every related FMEA, not just one document.
  • Lighter SME workload 200+ validation rules catch what a manual review might miss, before it reaches a reviewer.
  • Gets smarter over time The more of your data the agent sees, the better its recommendations.
  • Standards expertise built in Trained by the organization that helped write the FMEA standard, not a generic AI vendor.

Why Omnex

Omnex has supported FMEA since the 1980s through AQuA Pro and is on the writing/member committees for FMEA, SPC, MSA, SAE J1739, and ISO 13485/14971. 40+ years in business, 700+ employees in 30+ countries, 16 offices worldwide, 670,000+ platform users, 35,000+ clients including multiple Fortune 100 companies, and 500,000+ professionals trained in Omnex methodologies.

FAQs

Yes. Your historical FMEAs become the supervised-learning source of truth the agent reasons from, whether that data started in AQuA Pro, Excel, or PDF.

No. The agent already supports AIAG-VDA and AIAG 4th Edition formats out of the box; you can augment it to match your own templates without a separate training cycle.

Yes, at any point — in the chat before the FMEA is sent to AQuA Pro, or directly inside AQuA Pro afterward, the same as any manually created FMEA.

Both are supported. You can start a completely new production item from scratch, or attach the generated FMEA to an existing family, sub-family, or part.

Through a combination of retrieval-augmented generation (RAG) and fine-tuning on your own grounded data, plus a human-in-the-loop review step before any FMEA is finalized.

The underlying O-BOT platform is LLM-agnostic — the public demo used an OpenAI-based framework, but the architecture supports swapping in the large language model that fits your organization’s IT strategy.

Medical device teams typically use AIAG- or SAE-based FMEA structures, since ISO 14971 sets risk-management requirements but doesn’t define FMEA mechanics in detail. AQuA Pro supports both, and the rule set can be configured to your company’s own format.

Yes — Omnex offers a pilot so you can evaluate the agent against your own products and FMEA data before committing.

Yes. AQuA Pro has connectors and an API framework to bring in data from SAP and other ERP/PLM systems, and to push data downstream to MES and related systems.

Yes — Omnex’s e-mobility suite covers the full functional-safety work-product set, from HARA through FMEDA and DFMEA.