AI FMEA Software

AI is used where it is genuinely useful: extracting steps and functions from a process flow, drafting plausible failure modes, effects and causes, and proposing wording that an engineer can accept or reject. That is it. The risk math — Severity, Occurrence, Detection and Action Priority — is deterministic. The AI never decides whether a failure is High or Low.

This separation matters. AIAG-VDA Action Priority is a rule table, not a probability estimate. Letting a language model "guess" AP would defeat the purpose. See the AP guide for the full logic.

Most teams start with a spreadsheet template inherited from a previous project. It looks workable for one FMEA, then breaks the moment the team grows or a customer audit is announced. The failure pattern is always the same.

• Version control. "FMEA_final_v3_rev_after_meeting.xlsx" is not an audit trail. There is no reliable way to answer "what did we approve and when".
• Audit traceability. Reviewers cannot see who changed a rating or why. Inputs, controls and decisions are scattered across emails and chats.
• AP calculation mistakes. The AIAG-VDA Action Priority table is not a single formula — most templates fall back to RPN or implement AP partially. A 10-2-2 safety failure ends up as "Low" because the spreadsheet only multiplies.
• Collaboration. Two reviewers cannot edit the same matrix at the same time without overwriting each other. Cross-functional sessions degrade into screen-share marathons.
• Consistency. Severity scales drift between projects. Detection ratings inflate because no one defines what "automated" actually means.

See common FMEA mistakes for the engineering side of the same problem.

1. Inputs. Upload your process flow, drawings or a previous FMEA. Qhubio extracts steps, functions and known controls.
2. AI-assisted draft. The system proposes failure modes, effects and causes as a structured matrix — never as free-form prose.
3. Engineering moderation. The team reviews each row, adjusts wording, removes false positives, and confirms the controls actually in place.
4. Deterministic AP. Severity, Occurrence and Detection are rated once; Action Priority is calculated by the backend using AIAG-VDA 2019 rules. The math is never up to the spreadsheet.
5. Approval. Reviewer and approver sign off; the version is frozen and stored with input and output snapshots.
6. Export. One-click Excel and PDF for the customer file, the audit binder, or the Control Plan handoff.

• AIAG-VDA alignment. 7-step structure enforced from start to handoff.
• Action Priority engine. H / M / L calculated deterministically — Severity always dominates.
• PDF export. Customer-grade layout with revision history.
• Excel export. Three tiers (Basic / Advanced / Pro) for engineering, audits and OEM submissions.
• Team collaboration. Multiple reviewers, role separation between author, reviewer and approver.
• Version history. Every generation stores its inputs and outputs.
• Multi-language UI. EN, CZ, DE, FR, SK for cross-site teams.

• Automotive Tier-1 suppliers: PPAP-grade PFMEAs with AIAG-VDA AP, ready for OEM audits.
• CNC machining shops: Tool wear, dimensional drift and inspection capability handled as standard rows. See the worked example.
• Electronics manufacturing: Solder, AOI and ICT controls written as detection-by-design, not as inspection-only.
• Assembly operations: Poka-yoke and torque verification linked to detection rating.
• Medical devices: Risk file alignment between FMEA and ISO 14971 hazard analysis.
• Design engineering: DFMEAs that drive verification & validation, not paperwork.

AI-assisted does not mean unreviewed. The workflow assumes a qualified engineer reads every row, edits the wording, and explicitly assigns Severity, Occurrence and Detection. The tool blocks approval until at least one human reviewer signs off on the rated rows.

• Engineer review. Reads each row, removes irrelevant failure modes, rewrites wording that is too generic.
• Rating ownership. S, O, D are set by a human against the ranking tables. The tool never auto-fills ratings.
• Reviewer sign-off. A separate reviewer confirms the row applies, the controls match reality, and the AP makes sense.
• Approver release. Locks the version with its inputs and outputs for audit.

• Domain blind spots. The AI does not know your shop floor. Failure modes specific to a tool, fixture or operator practice will be missed.
• Plausible-but-wrong wording. Generic phrasing ("operator error", "improper handling") is forbidden by the AIAG-VDA guidance — engineers must rewrite.
• No real-world control verification. The AI proposes controls from the source documents; it cannot confirm they are actually deployed.
• No long-term memory. AI drafts do not learn from your previous FMEAs — the engineering review is what carries institutional knowledge forward.

• AIAG-VDA 7-step process — the methodology applied end-to-end.
• Action Priority guide — how the AP decision actually works.
• Severity, Occurrence and Detection rating guides.
• FMEA → Control Plan linkage — what flows downstream.
• PFMEA CNC example and more worked examples.
• Audit readiness checklist.