# AI for ProShop quality and documentation > Source: https://netray.co/erp-ai/proshop-quality-documentation-ai > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer AI on ProShop ERP reads parts, work orders, inspection plans, non-conformances, calibration, and controlled documents so a quality manager can answer audit questions and prepare first article packages from the shop's own records. Access is read-only through ProShop's API, with the AI stack hosted privately for controlled work. ## Kind use-case ## Erp Family other ## Erp Products - ProShop ERP ## Industries - Aerospace machining - Medical device machining - Defense suppliers - Precision manufacturing ## Region Global ## Persona Quality Manager ## Eyebrow ProShop + quality records ## Intro ProShop ERP was built around the idea that quality and documentation live next to the job. Parts, work instructions, inspection plans, first articles, non-conformance reports, gauge calibration, and training records are all in one system and tied to the work order. For aerospace and medical machine shops, that is the point. That also means a lot of records. When an auditor asks to see the inspection history for a part, the training record for the operator who made it, and the calibration status of the gauge used, the quality manager is stitching together records from several areas of the system, plus the customer's requirements, which sit in PDFs and emails. An AI layer can read those records and answer in plain language, showing the records behind each statement. It can help prepare a first article package, check whether a work order has the documents and inspections the customer requires, and summarize non-conformance trends. It does not approve anything. Quality decisions and signatures stay with qualified people. ProShop is a cloud-based system, which changes the hosting picture. This page explains how to read ProShop data safely through its API, where the AI stack should run for controlled work, and where ProShop's own features are already enough. ## Pain Points ### Audit evidence takes hours to assemble **Detail:** Pulling a part's inspection history, operator training, gauge calibration, and material certs for an auditor takes a quality manager an afternoon. ### First article packages are manual **Detail:** Building an AS9102-style FAI package means collecting ballooned drawing data, measurements, material certs, and process records from several places. ### Customer requirements in documents **Detail:** Flow-down requirements sit in purchase orders and specifications. Checking that each job meets them is a read-and-compare task that depends on memory. ### NCR trends not reviewed **Detail:** Non-conformance reports are written and closed, but patterns by part, machine, or supplier rarely get analyzed because it takes effort. ### Training and calibration gaps found late **Detail:** A lapsed gauge or an operator without current training is usually found by an auditor, not by the system. ## Use Cases ### Audit evidence assistant **Description:** The quality manager asks for the evidence trail for a part or work order. The assistant lists inspections, NCRs, gauge calibration status, operator training, and material certs with links to each record. **Erp Objects:** Parts, work orders, inspection records, NCRs, gauge records, training records, material certs **Outcome:** Evidence assembly that took hours becomes a short review. The records are still verified by the quality manager. ### FAI package preparation **Description:** The assistant gathers the measurements, drawing data, process records, and certs for a first article, and flags missing items before the package goes to the customer. **Erp Objects:** Part revision, inspection plan, measurement results, material certs, process routing **Outcome:** Fewer incomplete submissions. The FAI is reviewed and signed by a qualified person. ### Job requirement check **Description:** Before a job releases, the assistant compares customer purchase order requirements with the work order's documents, inspections, and certifications and lists gaps. **Erp Objects:** Customer PO notes, part requirements, work order documents, inspection plan **Outcome:** Gaps are caught before parts are made, not at final inspection. Rules are confirmed by the quality team. ### NCR trend summary **Description:** Across recent NCRs, the assistant groups causes by part, machine, supplier, or operator and highlights repeat issues. **Erp Objects:** Non-conformance reports, corrective actions, parts, work centers, suppliers **Outcome:** A starting point for corrective action reviews. Root cause decisions remain with the quality team. ### Calibration and training expiry watch **Description:** The assistant lists gauges and training items that are expired or about to expire, and which open jobs depend on them. **Erp Objects:** Gauge calibration records, training records, open work orders and inspection plans **Outcome:** Lapses are fixed before they affect parts. The system still enforces its own rules. ### Work instruction search **Description:** Operators ask how to perform a step, and the assistant answers from the approved work instructions for that part, citing the document and revision. **Erp Objects:** Work instructions, part process documents, document revisions **Outcome:** Faster, consistent answers, with revision control respected because only approved documents are indexed. ### Customer complaint and return lookup **Description:** When a customer reports an issue, the assistant retrieves the job history, inspection results, and related NCRs for the affected lot. **Erp Objects:** Shipments, work orders, inspection data, NCRs, customer records **Outcome:** A quicker first response with the right records on the table. Corrective action remains a human process. ## Architecture **Overview:** The AI stack runs in your environment and reads ProShop through its API with a scoped, read-only credential. It indexes only approved, current documents and keeps links back to the source records. **Layers:** #### Source access **Detail:** ProShop exposes an API that supports querying records. We use a read-only credential scoped to the objects needed and confirm available endpoints and rate limits with your account. #### Record sync and index **Detail:** Selected records and controlled documents are synced to a private store and indexed with revision and status, so the assistant cites current approved versions only. #### Quality query layer **Detail:** Tested queries for evidence trails, requirement checks, expiry lists, and NCR trends, with parameters filled by the model. #### Model serving **Detail:** Open-weight model on your GPU or private cloud. Because ProShop is cloud-based, only approved data is pulled into your environment. #### Controls and audit **Detail:** User mapping, question logging, and a strict boundary: the assistant drafts and cites, qualified people review and sign. ## Deployment Options ### On-prem or private server beside the ProShop database **Fit:** Shops that will not let job, customer, or drawing data leave the building, including ITAR and CUI work. **Detail:** An open-weight model (Qwen, Llama, Mistral, or gpt-oss class) is served with vLLM or Ollama on a single GPU server on the plant network. Retrieval and query tools run on the same box. ProShop itself stays in the vendor cloud, so the design question is which records you pull into your own environment and how often. ### Sovereign or private cloud tenant **Fit:** Multi-site manufacturers that want managed GPUs without sending data to a shared public AI API. **Detail:** The model and the vector index run in a private cloud account or a regional sovereign provider, reached from the plant over a private link. Data stays in a tenant you control and you choose the region. ### Read-only pilot against a replica **Fit:** Teams that want to prove value on real data before committing hardware or touching production. **Detail:** A nightly restore or replica of the ProShop data feeds a small model and a handful of approved questions. Nothing writes back. Most of the learning is about data quality and which questions people actually ask. ## Compliance ### The assistant gathers and checks records for an FAI package. A qualified person reviews and signs. It does not generate evidence that does not exist in the system. **Framework:** AS9100 and AS9102 first article inspection ### Document control stays in ProShop. The assistant indexes only approved current revisions and cites them, so answers follow controlled documents. **Framework:** ISO 13485 and FDA QMSR for medical device suppliers ### Controlled technical data should not go to a shared public AI service. The customer decides what to sync and hosts the AI stack within its own boundary. The customer remains responsible for its controls. **Framework:** ITAR and CUI ### Faster evidence retrieval supports audit preparation. Audit conclusions remain with the quality team and the auditors. **Framework:** NADCAP and customer audits ## Integration Notes - ProShop is cloud-hosted, so the integration is API based. Confirm available endpoints, authentication, and rate limits with your ProShop account before design. - Sync only what the use cases need, such as parts, work orders, inspection records, NCRs, gauge and training records, and approved documents. - Carry document revision and status into the index. Answers must never cite a superseded work instruction. - Check the contract and any customer flow-downs on where controlled technical data may be stored before syncing drawings. - Measurement data from CMMs and gauges may live outside ProShop. If so, include those sources deliberately. - Run the API sync on a schedule and log failures so the assistant never answers silently from stale data. ## Product Fit ### Custom build **Why:** ProShop is cloud-based, so a tailored private RAG over synced records, with revision-aware citations and checklist logic for FAI and requirement checks, is the best fit. ### DataRay **Why:** Helpful when customer specifications, certs, and emails sit in file shares alongside ProShop records. ## Engagement ### Discovery and data map **Duration:** 1 to 2 weeks **Deliverables:** - Inventory of the ProShop tables, views, and reports behind quality records and controlled documents - Ranked list of 15 to 25 real questions from the people who do the work - Decision on hosting, access method, and which roles may see which data - Written read-only boundary and approval rules for any later write-back ### Grounding and retrieval build **Duration:** 2 to 4 weeks **Deliverables:** - Read-only connection with a service account scoped to the approved objects - Curated query layer and document index for quality records and controlled documents - Evaluation set of questions with checked answers from your own data - Visible query or source citation on every answer ### Pilot with a small group **Duration:** 3 to 4 weeks **Deliverables:** - Pilot with 5 to 15 estimators, planners, supervisors, or quality staff - Weekly review of wrong or unhelpful answers and fixes - Usage log showing which questions were asked and which saved time ### Harden and hand over **Duration:** 2 to 3 weeks **Deliverables:** - Role mapping, audit logging, and backup of the AI stack - Runbook for model updates and index refresh after ProShop upgrades - Go or no-go note on any gated write-back actions ## Buyer Questions - Which ProShop API endpoints and limits apply to our account, and who approves access? - Which records and documents will be synced, and where are they stored? - How does the assistant ensure only current approved revisions are cited? - Does it ever sign, approve, or close a record? - How are ITAR or CUI drawings handled? - How do we handle measurement data kept outside ProShop? - How often does the sync run and what happens on failure? - How is accuracy measured on audit and FAI tasks? ## Faq ### Can AI approve an FAI or close an NCR? **Answer:** No. It can assemble the records and flag missing items, but a qualified person reviews and signs. Quality approvals carry legal and customer significance, so they stay with accountable people. ### ProShop is cloud-based. Can we still keep data private? **Answer:** Yes, by choosing what to pull. We read ProShop through its API with a scoped read-only credential and keep the synced records, index, and model in your own environment. You decide which records and documents are included. ### How does it handle document revisions? **Answer:** The index carries revision and status for each document and only cites approved current versions. If a superseded document is requested, the assistant says so. This matters for work instructions and inspection plans. ### Does ProShop already cover this? **Answer:** ProShop stores and links the records well, and its own search and reports cover many needs. An assistant adds value for cross-record questions, audit evidence assembly, and requirement checks that span documents and purchase orders. ### Can it read drawings? **Answer:** It can, if you choose to include them, but drawings are often controlled technical data. We recommend starting with structured records and approved work instructions, then adding drawings only after the hosting and contract questions are settled. ### How long does a pilot take? **Answer:** Usually eight to twelve weeks: confirm API access, sync a defined set of records, build the evidence and expiry queries, then test with the quality team on real audit and FAI scenarios. ### What are the risks? **Answer:** The main risks are stale sync data, wrong revisions, and overtrust. We address them with revision-aware indexing, visible citations, sync failure alerts, and a rule that people review and sign anything that matters. ## Search terms ProShop ERP AI, ProShop quality management AI, ProShop first article inspection AI, ProShop NCR analysis, AI for AS9100 machine shops, ProShop API integration, ProShop audit preparation, ProShop work instructions AI, ProShop calibration tracking, private AI for aerospace machine shop, ProShop document control AI --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json