QuotedPartAnswers · Updated 2026-10-30
How accurate is AI quoting software?
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AI quoting accuracy is not one percentage. Measure price error and time error on your own parts, against a defined reference such as the approved quote or the actual won price. Worked example: five test parts have quoted-versus-reference prices of $100 vs $110, $240 vs $220, $500 vs $515, $80 vs $80 and $1,000 vs $900. Mean absolute percentage error is (9.1% + 9.1% + 2.9% + 0% + 11.1%) ÷ 5 = 6.4%; the sample is too small to promise performance.
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Define the reference before testing
Choose whether the reference is a senior estimator’s blind estimate, a signed customer quote, the won price or actual fully loaded cost. Each answers a different question. A tool can match a historical price and still miss cost; it can estimate cost correctly and lose a bid because the market or schedule changed. Freeze the drawing revision, quantity, finish, delivery and scope for both methods.
Record errors in dollars and percentages. Percentage error makes a $1,000 part and an $80 part comparable, while dollar error shows exposure. Keep overestimates and underestimates separate; a mean near zero can hide large errors that cancel.
| Part | Reference | AI draft | Absolute % error |
|---|---|---|---|
| A | $110 | $100 | 9.1% |
| B | $220 | $240 | 9.1% |
| C | $515 | $500 | 2.9% |
| D | $80 | $80 | 0.0% |
| E | $900 | $1,000 | 11.1% |
| Mean | — | — | 6.4% |
Test the minutes and the assumptions, not just the final price
For each part, inspect material, setup, run, inspection, outside process and markup. A wrong finish lot minimum can be masked by a generous markup; a wrong cycle time can be masked by a low material price. Ask the estimator who ran the blind comparison to explain every difference without seeing which method produced it.
The example’s arithmetic uses reference as the denominator: for part A, |100 − 110| ÷ 110 = 9.1%. State the denominator in the test report. If a part is a new material or a five-axis route absent from the historical set, label it out-of-domain instead of folding it into the score.
Use guardrails around an AI draft
Require a human to confirm drawing revision, material grade, tolerance, quantities, controlled-data status, outside-process lead time and final price. Keep the original inputs and the reviewer’s edits. A model that refuses to quote an unclear print can be more useful than one that fills a blank with a confident number.
Report the sample size, date, part mix, error metric, outliers and exclusions. Do not turn five jobs into a product claim, and do not copy a vendor’s accuracy percentage without its method and scope.
What to do next
Run a blind test on at least one ordinary family and one difficult family, publish the method internally, and review the errors with the people who make the parts. Keep improving the routing evidence; accuracy is a property of the inputs, process and review as much as the software.
Illustrative accuracy-test data
- • Reference prices: $110, $220, $515, $80 and $900.
- • AI drafts: $100, $240, $500, $80 and $1,000.
- • MAPE uses |draft − reference| ÷ reference for each part.
| Metric | Value |
|---|---|
| Mean absolute percentage error | 6.4% |
| Largest percentage error | 11.1% on the $900 reference |
| Sample size | 5 parts; illustrative only |
Price one of your own parts
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