What an Automotive or Industrial RFQ Package Contains
A typical package holds a part print with GD&T and special characteristic symbols, material and coating specs, annual volume by year, release or ramp assumptions, target launch date, PPAP level, packaging and labeling specs, ship-to locations, tooling ownership terms, and a long terms and conditions document with warranty, price reduction, cancellation and intellectual property clauses. Industrial customers send simpler packages with a drawing, quantity and delivery. Both come as PDFs, spreadsheets and emails with revisions.
The estimator hunts through them for the facts that drive cost, and misses are expensive: a missed secondary operation or 100 percent inspection requirement changes the price.
What the Model Should Extract
A useful extraction is structured and cited to the page it came from. Material, gauge, tolerance on key features, hole sizes and positions, form radii, special characteristics, required secondary ops, plating or coating spec, packaging, annual volume by year, PPAP level, and delivery terms. The model should also list open questions, such as unclear datum scheme or missing volume.
Cited extraction is checkable by the estimator in seconds. An answer without a source page is not usable for quoting and should be rejected.
- Material, gauge, coating and tolerances with page references
- Annual volume by year and ramp assumptions
- PPAP level, special characteristics, 100 percent inspection or sort requirements
- Packaging, labeling, ship-to and tooling ownership terms
- Open questions to send back to the customer
Risk Flags Worth Automating
Some risks recur and are easy to screen for. Thin tolerances on formed features in springback-prone materials like high-strength steel or aluminum. Deep draw ratios that suggest a draw die and annealing.
Volume commitments that are too low to amortize a progressive die. Price-down clauses requiring annual reductions. Customer-owned tooling terms that leave you responsible for maintenance.
Unusual payment terms, warranty exposure and liability caps in the terms and conditions. The model should flag, not decide. A human with contract authority and an estimator with die experience review every flag.
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AI Quote Template and Prompt Set for Manufacturing
A fill-in manufacturing quote plus a reusable AI prompt that turns RFQ details into a structured cost build-up, with a mapping to common ERP quote fields.
Retrieval of Comparable Jobs and Draft Cost Build-Up
With access to the ERP and past quote files, the assistant can find jobs with similar material, gauge, size and operations and show actual press, SPM, scrap and setup. It then drafts a cost build-up using those actuals as the baseline, marked clearly as a draft with each number linked to its source job. If no comparable exists, the assistant says so and leaves the line blank for the estimator.
This protects against a confident but invented cycle time, which is the worst failure for any quoting tool.
Keeping Customer Data Private, and When Not to Bother
Customer prints and volumes are confidential, and automotive and defense programs often carry non-disclosure or export control restrictions. Running the model on your own servers, with open-weight models served locally, avoids sending packages to a third-party API, and role-based access keeps estimators to the programs they are allowed to see. Netray builds this pattern with read-only ERP access and human approval for write-back.
If you receive a few RFQs a month, the setup effort may exceed the benefit, and a good checklist and template will do more. AI starts to pay when RFQ volume, package size or estimator turnover is the bottleneck.
Frequently asked questions
Can AI read engineering prints for stamping quotes?
It can extract material, gauge, notes, tolerances and hole data from many prints, especially clean PDFs, with citations to the page. Accuracy drops on poor scans and dense GD&T, so the estimator should verify against the print. Treat extraction as a time saver for reading, not as a replacement for engineering review.
Is it safe to upload customer prints to a cloud AI tool?
That depends on your NDA, customer requirements and any export-control rules. Many stampers prefer to keep prints inside their own network by running open-weight models locally. Check your customer agreements before using any external service, and keep access logs for audits.
How much time does AI RFQ review save a stamping estimator?
It varies with package size and how clean the history is. The savings come mainly from reading, extraction and finding comparable jobs rather than from the pricing judgment itself. Measure it by timing a sample of quotes before and after, and watch quote-to-actual variance, not only speed.
Send Netray a redacted RFQ and see the extraction, risk flags and comparable-job retrieval run on a private system.