01
A lost quote can be a good decision.
A price can be disciplined, economically sound and still lose. Lowering it might only have won unprofitable work.
Independent early-stage research
You know whether you won the quote.
Do you know whether it was a good decision?
Quote Decision Lab is researching how custom manufacturers learn from the full path between a quote and the job's actual economic result—without asking shops to hand over confidential customer information.
No CRM passwordsWe do not need system access.
No drawingsNo CAD or proprietary part files.
No customer namesInitial research can stay anonymous.
No sales presentationThis stage is about understanding the problem.
What we're studying
The learning loop after the quote leaves the estimator's desk.
Many systems help create a quote. We're interested in something different: how a shop determines whether the original assumptions were right after the quote is won, lost, produced and finally measured against reality.
01Quote
02Won / Lost
03Production
04Actual setup
05Actual runtime
06Scrap / rework
07Final margin
The core hypothesis
Winning is not the same as quoting well.
02
A won quote can be a bad decision.
A strong win rate can hide underestimated setup, runtime, outside processing, scrap or customer-specific margin leakage.
03
The lesson may never reach the next quote.
Actual production knowledge can exist inside the shop without reliably making its way back to the next estimating decision.
The questions
What we want experienced shops to teach us.
We're not asking shops to prove our idea. We want to understand how this actually works on the floor, in estimating and in management.
When you lose a quote, how do you know whether your price was actually wrong?
When you win a quote, how do you know you priced it correctly?
Do you compare quoted setup, runtime and material assumptions with actual job performance afterward?
How often does that happen systematically versus only when something goes badly?
Can you identify customers with high win rates but poor realized margins?
Which estimator is actually best: the one with the highest win rate, or the one whose jobs produce the strongest realized margin?
When material, labour or outside-process costs change, how quickly do those lessons make it back into quoting?
Can an estimator easily find and use similar historical jobs when quoting something new?
What quoting mistake cost your shop the most money during the last year?
Privacy first
We can learn without asking for your confidential business data.
For an initial research conversation, we do not need customer identities, drawings, part numbers, CRM credentials, proprietary files or detailed financial statements.
Conversation first. We start by understanding your process, not requesting exports.
You control what is shared. Examples can be described verbally or anonymized.
No automated customer contact. This research is about decision learning, not messaging your customers.
No claim that we know your shop. You know machining. We're investigating whether the feedback loop around quoting can be improved.
Help shape the research
If you quote custom work, we'd like to understand how your shop learns from the jobs it wins—and the jobs it loses.
A 15–20 minute conversation is enough. No preparation, no data upload, and no sales demo.