AI-assisted quoting & estimation
A manufacturing business where this lands in Sales.
02 — What is actually going wrong
RFQs pile up waiting for the one estimator who knows the machines. Quotes go out in a week when the customer wanted them in a day, and by the time yours arrives the buyer has already anchored on somebody else's number. You lose work you could have made profitably, on timing alone.
How it works
The incoming RFQ and drawings are parsed for features, materials and tolerances, matched against the most similar jobs you have actually run, and priced off your real cost model rather than a rule of thumb. The estimator gets a draft quote with the comparable jobs shown, and adjusts.
What good looks like
Standard RFQs quoted same-day. Your estimator spends their time on the genuinely novel parts, and win rate rises because you are consistently first in with a defensible number.
03 — The problem, in money
- RFQs received per week30 rfqs250 rfqsPlaceholder — not a validated benchmarkStarting point
- Extra win rate from faster quotes0.05 ratio0.5 ratioPlaceholder — not a validated benchmarkStarting point
- Average order value8,500 USD125,500 USDPlaceholder — not a validated benchmarkStarting point
04 — The options
05 — The call
Build it. A custom system is the right move here — the payback justifies owning it rather than renting a compromise.
06 — How this goes wrong
- Quoting from a cost model that was never accurate. Automation makes a wrong margin wrong faster and at scale — validate the cost model before anything else.
- Similarity matching that ignores tolerance. Two parts can look identical and differ threefold in cost because one is held to a tighter spec.
- Letting quotes go out unreviewed to win the speed metric. One underpriced long-run job erases a year of the gain.
- Not capturing why quotes were lost. Without that feedback the system optimises for output, not for winning.
07 — How you would actually do it
- 01Audit the cost model against actual job costing for the last twenty completed jobs. Most shops find a gap here and it is the real project.
- 02Digitise the historical job archive enough to be searchable — this is usually the long pole.
- 03Build feature extraction for your dominant part families first.
- 04Run in shadow against live RFQs and compare the draft to what the estimator actually quoted.
- 05Release for standard parts only, with estimator sign-off retained.
- 06Log win/loss with the competing price where you can get it.
08 — Tools worth looking at
Named for orientation, not endorsement. No affiliate arrangements, and nothing here has been paid for.
09 — Run it on your numbers
Start from industry averages, then drag the inputs to match your business. The number moves live.Drag each input to your number — the starting points are neutral midpoints, not industry data. The number moves live.
10 — The evidence · what others report
Reported numbers are third-party claims, source-attributed — not verified by me, and never blended into the model above.
11 — Questions owners ask
Is this your business?
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