Built for the moment a customer complaint becomes fab risk.
Logging the case is not the same as running the response. FabQED™ gives semiconductor suppliers one accountable path from first signal through investigation, customer communication, corrective action, and signed proof of closure.
The case can be visible while the response remains fragmented.
Customer pressure moves faster than evidence scattered across inboxes, meetings, file stores, quality systems, and engineering tools.
The mark at the end of a completed proof.
Quod erat demonstrandum means “that which was to be demonstrated.” A fab customer does not accept a claim that a problem is contained, a root cause is found, or a corrective action holds. The response must be supported, reviewed, and safe to communicate.
The signal, containment, investigation, action, commitment, and closure remain connected.
Owners, due work, blockers, evidence asks, and customer commitments stay visible across functions.
Internal alignment and customer updates stay bounded by reviewed facts and known uncertainty.
Signed decisions, supporting evidence, corrective action, and verification form the closure record.
Semiconductors first, where technical ambiguity and customer consequence meet.
Surface, consumable, film-stack, polisher, and post-CMP clean interactions.
Chemistry, track, scanner, transfer, metrology, and defect-signature interactions.
Interface, assembly, bond, underfill, warpage, reliability, and qualification interactions.
supporting fab-grade response orchestration and integrated domain intelligence.
Built for work that exposes process weakness, customer risk, and qualification pressure.
The operating record is designed to remain under customer custody. AI can assist with context and preparation; it does not own the record or the decision.
- Customer-boundary deployment
- Designed for customer cloud, network, and operated-environment deployment.
- Human authority
- People retain final authority to release, sign, send, approve, and close.
- Customer-approved AI posture
- AI participation follows customer policy and can be limited or disabled.
- No silent training
- Customer confidential data is not used to train shared models without explicit permission.