# AI for Casting RFQ and Drawing Review > Source: https://netray.co/resources/ai-for-casting-rfq-and-drawing-review > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Knowledge Base ## Short answer How a private AI reads casting RFQ packages, prints and specs, flags alloy, NDT and tolerance risks, and drafts questions for the customer before you quote. ## Category ai-automation ## Content **Intro:** AI for casting RFQ review reads the drawing, specification callouts, quality requirements and commercial terms, extracts the requirements that drive cost and risk, and flags questions to resolve before quoting. For a foundry or die caster, that means catching the alloy and temper requirement, tolerance class, radiography or pressure test requirement, surface finish, machining stock and annual volume early. It speeds up the first read, but it works only if run privately on controlled drawings and reviewed by someone who knows castings. **Sections:** #### What Is Hidden in a Casting RFQ **Body:** Casting prints carry many cost drivers in small text: alloy and temper callout, general tolerance block referencing a standard such as NADCA for die castings or ISO 8062 for castings generally, critical dimensions marked for inspection, datum scheme, machine stock allowance, impregnation or pressure test requirements, NDT class and acceptance criteria, and cosmetic zones. The PO terms may add PPAP level, first article requirements, packaging and ship-to-line demands. An experienced estimator catches these, but the first read is slow and easy to rush during a busy week. **Bullets:** - Alloy, temper and governing specification - General tolerance standard and critical-to-quality dimensions - NDT, leak test or pressure test requirements and acceptance level - PPAP, first article and certification requirements #### Risk Flags That Prevent Bad Quotes **Body:** A useful assistant does more than extract text. It flags combinations that raise scrap or cost: thin walls plus tight flatness, porosity-free requirements on a pressure-bearing feature, tolerances tighter than the standard process capability, deep undercuts requiring slides, or surface cosmetic requirements on a die cast face. It can compare against your own history, for example parts with similar wall thickness that ran at high scrap, and ask the estimator to confirm. The goal is a short list of questions for the customer or design engineer, sent before the quote rather than after the first article fails. #### Casting Is Not Machining: Why Generic Tools Fall Short **Body:** Many quoting tools were built for CNC machining and assume subtractive geometry. Casting economics hinge on gating, yield, solidification, tooling and shrinkage allowance, none of which appear on the print. A useful AI for foundries needs to be grounded in your data on actual yield, scrap by feature type, pattern cost history and process capabilities, not generic machining logic. It should also respect design-for-casting feedback, such as suggesting draft angle, uniform wall thickness or radii changes to the customer as optional value engineering notes. **Bullets:** - Ground suggestions in your own yield and scrap history - Suggest design-for-casting changes as customer notes, not quote changes - Cite the print page or note number behind every extracted requirement - Keep tooling and piece price logic separate #### Keeping Controlled Drawings Private **Body:** Many casting RFQs come from defense, aerospace and energy customers and can carry export-controlled technical data. Uploading those drawings to a public AI service can create compliance exposure for you as the recipient. A private deployment runs open-weight models on your own servers or in a private cloud you control, with role-based access, logs and no external calls. That architecture lets your compliance lead apply your existing technology control practices. Netray does not itself provide a compliance certification, the controls remain yours to define and audit. #### When Manual Review Is Fine **Body:** If you see a handful of RFQs a week, most from repeat customers with familiar prints, an AI reader adds little over an experienced estimator. It earns its keep when RFQ volume is high, packages are long, estimators are scarce, or new customers arrive with unfamiliar specs. Try it first on past RFQs where you know the outcome and see whether it finds the requirements that mattered. **Cta:** We can run a private pilot on a set of your past casting RFQs and show what it extracts and flags, with citations, before you commit to anything. **Faq:** #### Can AI read casting drawings? **Answer:** Current models can read text, notes and tables from clean drawings and PDFs reasonably well, but handwriting, poor scans and dense GD&T need human verification. Treat extractions as a draft with citations to check, not a final answer. #### What standards govern die casting tolerances? **Answer:** NADCA publishes product specification standards covering die casting tolerances, draft and other design guidance, and many customer prints reference them. Other casting processes often reference ISO 8062 or customer-specific standards. #### Is it safe to upload casting prints to a public AI tool? **Answer:** Not if the drawings are export-controlled or contractually confidential. Check your customer agreements and compliance obligations first. A private deployment on your own infrastructure avoids sending data to third parties. #### Will AI suggest design changes to my customer? **Answer:** It can draft optional design-for-casting notes such as draft angle or wall thickness suggestions, but your engineer should decide what to send. Customers own their designs and may not accept changes. ## Related Slugs - ai-quoting-aerospace-defense-manufacturers - casting-quote-estimating-for-foundries - air-gapped-llm-deployment-patterns - human-in-the-loop-design-patterns ## Search terms AI for RFQ review, casting drawing review, AI casting quote, foundry RFQ software, NADCA tolerances, private AI foundry, AI read engineering drawings castings --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json