# AI for Spring and Fastener RFQ Review > Source: https://netray.co/resources/ai-for-spring-and-fastener-rfq-review > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Knowledge Base ## Short answer How spring and fastener makers use private AI to read RFQs and prints, find comparable past jobs, screen cert and spec requirements and draft quotes for approval. ## Category ai-automation ## Content **Intro:** AI for RFQ review in a spring or fastener shop reads the incoming package (print, specification, purchase terms, quantity and delivery request), extracts the parameters that drive cost, finds similar past jobs in your ERP with their actual costs, screens for cert and standard requirements, and drafts a quote for an estimator to approve. It does not set price or decide whether to bid. The value is speed and consistency when RFQ volume is high or estimating knowledge sits with one or two people. This guide covers what the workflow looks like, what data it needs and where it is not worth the effort. **Sections:** #### What Arrives in a Spring or Fastener RFQ **Body:** A spring RFQ might be a PDF print with wire diameter, material, coils, free length and load requirements, a quantity ladder, and a note about packaging. A fastener RFQ may be a drawing or a standard callout such as an ASTM grade, a finish specification and an annual volume with release schedule. Both frequently come with customer quality requirements, certs required, PPAP level, and sometimes country-of-melt restrictions. Distributors and OEMs send these by email with attachments, and estimators re-key the details into the ERP or a spreadsheet before they can begin costing. #### Extraction: Turning a Print Into Cost Drivers **Body:** An AI model with document understanding can read a drawing or spec and propose structured fields: material and wire size, dimensions, tolerances, special processes, standards referenced, required certs and quantity breaks. Each extracted value should show the place on the document it came from, so the estimator can verify in seconds. Drawings are imperfect inputs, with scanned prints, handwritten notes and non-standard views, so expect some fields to be flagged as uncertain rather than guessed. Treat the output as a prepared worksheet, not a finished estimate. **Bullets:** - Fields extracted with source location shown - Uncertain values flagged rather than guessed - Customer spec and cert requirements pulled into a checklist #### Matching Past Jobs and Actual Costs **Body:** The most useful step is retrieval: find the five most similar parts the shop has made, by wire size, material, coil count, machine, or in the fastener case thread size, length, grade and finish, and show their actual cost, setup, scrap and run rate. This is what an experienced estimator does from memory. An AI assistant that queries your ERP's work order history can do it for every RFQ, including for estimators who have not seen the part family before. It depends on data quality: if work orders are closed without accurate hours or scrap, the matches will be misleading, and cleaning that is the first task. #### Screening Specs, Certs and Contract Terms **Body:** Before pricing, the RFQ needs a requirements check: does the spec call for a material you can source, a test you can perform, a finishing process you can outsource, a cert format you can issue? Does the purchase terms document add flow-downs such as DFARS specialty metals, country of origin, or customer quality manual clauses? A model can compare the RFQ against a library of what the shop accepts and flag exceptions for the quality manager. In defense work, keep drawings and controlled data inside your own network, since sending ITAR technical data to a public AI service is a compliance problem, which is why deployment on your own hardware matters. #### Draft Quote, Human Approval, and When to Skip It **Body:** The output is a draft: line items for material, setup, run, secondary operations, outside processing, certs and packaging, each tied to a source, for the estimator to adjust and approve. No quote goes out, and nothing writes to the ERP, without that approval. Skip this if you receive few RFQs, if the work is mostly repeat parts with set prices, or if a simpler quoting tool already handles your catalog. It is most useful when you get dozens of RFQs a week with long-tail parts, or when turnaround time is losing you work. **Cta:** Send Netray a sample spring or fastener RFQ and see the extraction, comparable jobs and draft quote produced on infrastructure you control. **Faq:** #### Can AI read a spring print and quote it? **Answer:** It can extract wire size, material, coils, dimensions and load requirements from a print and draft a quote using your past job data, but an estimator should verify the extraction and approve price. It is a time saver, not an autopilot. #### Is it safe to upload customer drawings to an AI tool? **Answer:** Not if the drawings are export-controlled or carry confidentiality terms. Run the model on your own hardware or private cloud so drawings never go to a third-party service, and limit access by role. #### What data do I need before AI quoting helps? **Answer:** Reasonably accurate work order history: material used, hours by operation, scrap and outside processing cost by part. If that data is poor, fix it first, because comparisons will otherwise mislead. ## Related Slugs - ai-rfq-review-for-metal-stamping-suppliers - ai-quoting-aerospace-defense-manufacturers - how-to-quote-custom-compression-springs-from-a-print - itar-compliant-ai-tools-manufacturing ## Search terms AI RFQ review manufacturing, AI quoting for spring manufacturers, AI fastener quote, automated RFQ extraction drawings, AI estimating small manufacturer, private AI RFQ ITAR --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json