# AI for AS9100 Quality Management on Your ERP > Source: https://netray.co/erp-ai/ai-for-erp-as9100-aerospace-suppliers > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer For an AS9100D-certified supplier, the appeal of AI in the quality module comes with a specific condition: every drafted NCR, CAPA, or FAI summary has to trace back to a source ERP or inspection record an auditor can independently verify, or it is not usable. The practical pattern is grounding AI drafting tools directly in ERP quality data (inspection lots, NCR history, BOM and routing) with full logging of what the model read and generated, so the output speeds up documentation without becoming a black box a QMS auditor cannot follow. Deployed on-prem or in a controlled private environment, this also keeps ITAR-adjacent technical data inside the same boundary your quality and export control programs already manage. ## Kind persona ## Erp Family multi-erp ## Erp Products - SAP S/4HANA - Infor SyteLine - Infor LN - Epicor Kinetic - IFS Cloud ## Industries - Aerospace - Defense ## Region United States ## Persona Quality Director ## Eyebrow AS9100D + on-prem AI ## Intro AS9100D audits do not get easier with volume. Nonconformance reports pile up, First Article Inspection packages (AS9102) require pulling data from a dozen places into three forms, and every corrective action needs a root cause narrative that will hold up months later when an auditor asks about it. A Quality Director evaluating AI is not looking for a magic fix, they are looking for something that removes the mechanical parts of this work without introducing a new source of audit findings. The specific risk with AI in a quality system is trust. An auditor does not accept an AI-generated FAI number or NCR root cause on faith any more than they would accept an unverified spreadsheet. If the AI tool cannot show exactly which ERP records, drawings, and inspection results it drew from, it creates more work, not less, because every output needs manual verification before anyone can rely on it. There is also an overlap most quality teams do not think about until it comes up: a fair amount of the data feeding NCR, CAPA, and FAI documentation - part numbers, drawings, supplier certifications - can carry export control sensitivity in an aerospace or defense supply chain. An AI tool built for quality documentation has to respect the same access boundaries the rest of your technical data already lives inside. This page covers where AI genuinely helps a quality function running on SAP, Infor LN or SyteLine, Epicor Kinetic, or IFS Cloud, what the audit trail needs to look like for an output to be trustworthy, and the questions worth asking before letting an AI tool touch anything that ends up in a customer-facing quality record. ## Pain Points ### AS9100 audit prep is a document control marathon **Detail:** Pulling together evidence of closed NCRs, CAPA effectiveness, and document control history for an audit consumes weeks of quality staff time that could go toward actual process improvement. ### FAI packages require manual compilation from scattered sources **Detail:** An AS9102 Form 1/2/3 package pulls from the BOM, the routing, inspection results, and the drawing, and assembling it correctly is still largely a manual, error-prone exercise. ### Counterfeit parts risk on open-market buys **Detail:** AS6081 and AS5553 expectations for counterfeit parts avoidance require extra scrutiny on non-franchised purchases, and that screening often depends on a purchasing agent remembering to flag it. ### Traceability gaps between ERP and MES **Detail:** Lot and serial traceability required for AS9100 clause 8.5.2 often spans both the ERP and a separate MES or paper travelers, making a complete traceability record harder to assemble than it should be. ### Auditors want evidence a controlled process, not a black box **Detail:** If a quality team starts using AI to draft records, an auditor will ask how the output is verified and traced back to source data, and I don't know is not an acceptable answer. ## Use Cases ### NCR and CAPA drafting from quality records **Description:** Draft an initial nonconformance description and 8D-style corrective action narrative from the ERP's NCR record and related inspection data, for a quality engineer to review and finalize. **Erp Objects:** NCR/disposition records, inspection lot data, affected part and routing history **Outcome:** reduces the drafting time for routine NCR and CAPA documentation ### FAI Form 3 draft compilation **Description:** Pull BOM, routing, and inspection result data together into a draft AS9102 Form 3 characteristic list, for the quality engineer to verify against the drawing before submission. **Erp Objects:** BOM, routing operations, inspection results, drawing revision references **Outcome:** cuts the manual compilation time for each characteristic on a routine FAI ### Supplier corrective action request drafting **Description:** Draft a SCAR based on a supplier-caused nonconformance record, including relevant purchase order and receiving inspection history. **Erp Objects:** Supplier NCR records, purchase order history, receiving inspection data **Outcome:** shortens turnaround on issuing a SCAR after a supplier-related nonconformance ### Counterfeit parts risk flagging on open-market purchases **Description:** Flag purchase requisitions for non-franchised or open-market sourcing of parts, prompting the counterfeit parts avoidance procedure before the PO is issued. **Erp Objects:** Purchase requisition, vendor master (franchised vs. open-market flag), item master **Outcome:** reduces missed counterfeit parts screening on non-routine purchases ### Calibration and gage due date summarization **Description:** Summarize upcoming and overdue calibration due dates across measurement equipment for the quality team's weekly review. **Erp Objects:** Gage/calibration tracking records, work center equipment assignments **Outcome:** reduces the manual effort of compiling a calibration status report ### Document control gap analysis against AS9100 clauses **Description:** Compare current document control records against AS9100D clause requirements to flag gaps ahead of an internal or external audit. **Erp Objects:** Document control records, revision history, approval records **Outcome:** surfaces document control gaps before an auditor finds them ### Grounded audit readiness Q&A **Description:** Answer plain-language questions from quality staff about NCR history, CAPA status, or traceability, with every answer citing the specific ERP record it came from. **Erp Objects:** NCR, CAPA, and traceability records across the quality module **Outcome:** speeds up internal audit prep by reducing manual report-building ## Architecture **Overview:** Every layer is built around a single requirement: any AI-drafted quality document has to be traceable back to the exact source ERP records it used, because that traceability is what makes the output usable in an AS9100 environment rather than just a convenience feature. **Layers:** #### ERP connectors **Detail:** Read-only connections to the quality module (NCR, CAPA, inspection, calibration tables) and related BOM/routing data, whether the ERP is SAP QM, Infor LN or SyteLine quality management, Epicor Kinetic, or IFS Cloud. #### Data and semantic layer **Detail:** Maps quality module fields and AS9100 clause references into a consistent structure, so drafted documents can cite both the source record and the relevant clause. #### Model serving **Detail:** Open-weight models served on-prem or in a private cloud, keeping export-sensitive part and drawing data inside the same boundary your export control program already manages. #### Retrieval and agents **Detail:** Narrow drafting agents (NCR, FAI, SCAR) retrieve only the specific records relevant to the document being drafted, with every citation preserved in the output for verification. #### Governance and audit **Detail:** Every drafted document logs the exact source records used, and requires a human quality engineer's review and sign-off before it becomes part of the official record. ## Deployment Options ### Air-gapped on-prem **Fit:** Suppliers whose customer flow-down clauses already require no external network path for technical or export-controlled data **Detail:** Keeps quality drafting entirely inside the facility network, consistent with the same boundary already protecting drawings and technical data. ### Private cloud **Fit:** Multi-site suppliers wanting centralized quality AI across facilities without losing control of hosting and access **Detail:** A customer-controlled tenancy lets a Quality Director standardize NCR and FAI drafting practices across sites while keeping data access under your own control. ### Hybrid **Fit:** Suppliers where only some product lines carry ITAR or CUI exposure **Detail:** Non-sensitive quality data can be served more broadly while export-controlled product lines stay on the more restrictive deployment pattern. ## Compliance ### AI-drafted documents preserve full traceability to source ERP and inspection records, supporting clause 8.7 nonconforming output control and clause 9.2 internal audit evidence requirements rather than obscuring them. **Framework:** AS9100D ### Draft FAI compilations pull directly from BOM, routing, and inspection data, with every characteristic traceable to its source, so the quality engineer's verification step is faster, not skipped. **Framework:** AS9102 (First Article Inspection) ### Automated flagging of open-market and non-franchised purchases supports the counterfeit parts avoidance procedure your quality system already documents. **Framework:** AS6081 / AS5553 (counterfeit parts avoidance) ### Where quality records reference export-controlled technical data, AI drafting stays inside the same boundary your Technology Control Plan already governs. **Framework:** ITAR (where applicable) ### Deployment options are chosen to match the strictest customer-specific quality and data handling requirements your contracts already carry. **Framework:** Customer flow-down quality clauses ## Integration Notes AI-drafted NCR, CAPA, and FAI content is written back to the ERP only after a human quality engineer approves it, never automatically closing or submitting a record. Every drafted document includes inline citations to the specific ERP record, inspection result, or drawing revision it drew from, so verification is a lookup rather than a re-investigation. Connections to the quality module are read-only; write-back for approved content uses the same ERP transaction paths quality staff already use manually. Counterfeit parts flagging runs against existing vendor master franchise/distributor attributes, so it does not require a new data source, only a new check on existing fields. Calibration and gage due date summaries pull directly from the ERP's existing calibration tracking tables, avoiding a separate spreadsheet-based tracking system. For export-sensitive product lines, the AI system runs on the same network segment as other technical-data-handling systems, keeping the access boundary consistent. Model outputs are logged with the source records used, giving the quality team an audit trail an AS9100 assessor can review alongside the finished document. ## Product Fit ### Custom build **Why:** NCR, CAPA, FAI, and SCAR drafting need to match your specific quality manual language and ERP field layout closely enough that a purpose-built agent outperforms a generic drafting tool. ### ERPray **Why:** Grounded, citation-backed Q&A over quality module data supports audit prep and day-to-day quality engineering questions with full traceability to source records. ### SyteRay **Why:** For suppliers running Infor SyteLine or CloudSuite Industrial specifically, SyteRay's SyteLine quality management module familiarity complements the broader drafting and audit architecture. ## Engagement ### Discovery **Duration:** 2-3 weeks **Deliverables:** - Review of current quality module configuration and NCR/CAPA/FAI process flow - Interview with the Quality Director on audit history and recurring pain points - Assessment of export control overlap with quality documentation - Draft architecture and deployment recommendation ### Pilot **Duration:** 6-8 weeks **Deliverables:** - NCR or CAPA drafting live for one product line or work center - Citation and audit trail format validated with quality staff - Human review workflow tested end to end - Pilot results documented for internal audit readiness review ### Production **Duration:** 8-12 weeks **Deliverables:** - Pilot use cases extended to additional product lines - FAI drafting support added if not part of the pilot - Counterfeit parts flagging enabled on purchase requisitions - Quality manual references updated to reflect the AI-assisted step and its review requirement ### Scale **Duration:** ongoing **Deliverables:** - Rollout to additional facilities or product lines - SCAR and calibration summarization added as needed - Periodic review of drafted-document accuracy against final approved versions - Support ahead of surveillance and recertification audits ## Buyer Questions - Can I show my auditor exactly which ERP records and drawings a drafted NCR or FAI came from? - What is the required human review step before an AI-drafted document becomes part of the official quality record? - How does the system prevent hallucinated characteristic values on an FAI draft? - Does the system support air-gapped deployment if our export control program requires it? - How is the counterfeit parts flagging logic configured, and can we adjust it to our procedure? - What happens to drafted-but-unapproved documents if a user never finalizes them? - Can the system's audit trail be exported in a format our AS9100 registrar would accept as supporting evidence? - What is the ongoing cost structure, and does it scale with number of NCRs or with number of users? ## Faq ### Will an AS9100 auditor accept an AI-drafted NCR or CAPA? **Answer:** Auditors accept the finished, human-reviewed and approved record, the same as they always have; what matters is that your quality engineer verified and signed off on the content, and that you can show the source data behind it if asked. AI drafting is a time-saving step before that review, not a replacement for it. ### How do we prevent an AI tool from putting a wrong number on an FAI form? **Answer:** The draft should pull characteristic values directly from ERP inspection results and drawing references rather than generating them, so a wrong number would trace back to a data entry issue, not model invention. The quality engineer's verification step against the actual drawing remains mandatory before submission. ### Does this help with counterfeit parts avoidance under AS6081 or AS5553? **Answer:** It can automate the flagging step that prompts your existing counterfeit parts avoidance procedure, catching open-market or non-franchised purchases that might otherwise slip through if a purchasing agent forgets to flag them, but it does not replace the procedure or the technical screening it requires. ### Can AI help close the gap between ERP traceability and MES-based traveler data? **Answer:** Yes, if both systems are connected to the same retrieval layer, an AI tool can pull lot and serial data from both sources into a single traceability summary, which is often faster than manually reconciling an ERP report against paper or MES travelers. ### Is this only useful for large aerospace suppliers, or does it work for a small AS9100 shop? **Answer:** It scales down reasonably well. A small shop with a handful of quality staff often gets proportionally more benefit from NCR and FAI drafting assistance, since a smaller team has less slack to absorb the manual documentation burden during a busy audit cycle. ### How does export control fit into a quality-focused AI deployment? **Answer:** If drawings or part data referenced in quality records are export-controlled, the AI system needs to run inside the same access boundary your Technology Control Plan already defines, so quality staff outside that boundary would not have their queries touch that data. ### What is the realistic time savings for NCR and FAI drafting? **Answer:** It varies by document complexity, but drafting assistance typically saves the most time on routine, well-precedented nonconformances and characteristic-heavy FAI packages, where the mechanical work of pulling data together dominates the time, rather than on genuinely novel root cause investigations. ## Search terms AI for AS9100 quality, AS9100 AI ERP, aerospace supplier AI, NCR CAPA AI drafting, FAI AS9102 AI assistant, counterfeit parts AI screening, AS9100 quality management AI, aerospace ERP AI, quality director AI ERP, AS9100 traceability AI, AI for aerospace machine shops, on-prem AI quality management --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json