AI DFM & Quotation Check

Q: Is this one of those instant-quoting portals?

No — and the difference matters for your business. Instant-quoting platforms compute prices from geometry for commodity parts; they collapse exactly where your margin lives: complex parts, judgement about risk, capacity and customer context. Our system is decision support for professionals: it gathers and checks the facts (feasibility, capability fit, references, cost frame), and your estimator makes the call. Nothing goes to a customer that a human hasn't decided.

Q: Where does the "capability profile" come from — is that a big data project?

Smaller than you'd fear, and it's part of the pilot, not a precondition. Machine envelopes and process lists usually exist; achievable-tolerance tables are drafted per process in workshops with your AV and refined against real outcomes (the hindsight validation on 20 historical RFQs does much of the calibrating). The profile is versioned and yours — and it tends to become a valuable document in its own right, because most companies have never written down what they can actually do.

Q: How good is the extraction from customer drawings?

Good enough to triage with, honest enough to trust: every extracted value carries a confidence score and a link to its spot on the drawing, low-confidence items are flagged, and the verdict shows which facts it rests on. In the pilot we measure extraction accuracy on your real RFQ documents — and because the triage is a recommendation, an extraction miss costs a correction, not a wrong quote.

Q: Can we trust a no-bid recommendation?

You shouldn't trust it — you should check it, which takes two minutes because the evidence is attached: the envelope that's exceeded, the process you don't have, the reference that lost money. That's the design principle throughout: recommendations are cheap to verify and impossible to follow blindly. In hindsight validation, no-bid recommendations are tested against enquiries you actually worked and lost — so you see the would-have-saved hours before you rely on it.

Q: Our RFQs are under customer NDA. Where does processing run?

On your infrastructure — on-premises or in an EU cloud under your control. Customer drawings and your post-calculations are exactly the two datasets that must never reach a public AI service, and the architecture treats both as such from day one.

Q: How does this relate to your tender engineering offer for the energy sector?

Same family, different battlefield. Tender engineering (energy) digests 200-page utility specifications into compliance matrices — the pain is document mass and requirement deviation. The quotation check (this page) is built for machine builders and component makers: many smaller RFQs, and the pain is feasibility and cost against your own manufacturing reality. If your business sees both patterns, the two share infrastructure — extraction, similarity search, on-prem deployment — and we scope them together.

Seven out of ten quotes are engineered for orders you never win. Stop paying full engineering price for them.

Engineering-to-order has a quiet margin leak, and it sits at the quotation desk. Every serious RFQ — a component family, a weldment, a machined part, a subassembly — costs days of skilled work before any revenue exists: reading drawings, judging whether your machines can hold the tolerances, estimating cost, writing the offer. At typical ETO hit rates below 30 percent, most of that effort is spent on bids that go nowhere — while enquiries you could have won wait in the queue.

The bitter part: the highest-leverage decision in the whole process — quote, quote with deviations, or no-bid — is made earliest, with the least information, under the most time pressure. It depends on knowledge that lives in three places: the RFQ documents, your real manufacturing capabilities, and your part history. All three are data. None of them are connected.

We connect them: AI extracts the technical content from incoming RFQs, checks it against your actual capabilities — machine envelopes, achievable tolerances, materials, processes — finds the most similar parts you have already made (with their post-calculations), and hands your estimator a triage verdict with evidence. In minutes, not days.

// The quotation margin leak, in numbers

<30%

typical ETO hit rate — the majority of quotation engineering serves lost bids

of quotation effort spent on orders that never come — unpriced, unbudgeted, accepted as fate

2–5 days

of engineering per serious quote: drawing review, feasibility, costing, writing

minutes

for an AI triage verdict with evidence — before your engineers invest a single hour

The no-bid decision is your most expensive gut feeling

Ask your quotation team how they decide which enquiries get worked first — and which get declined. The honest answer, in most machine-building and component businesses: experience, gut feeling, and whoever shouts loudest. Not because your estimators lack judgement, but because the information their judgement needs is expensive to gather. Whether a tolerance is comfortably within your machines’ capability or right at the edge, whether that exotic material has ever been through your shop, whether a nearly identical part was quoted two years ago and lost on price — each of those answers exists somewhere, and each costs hours to dig up.

So the digging only happens for enquiries someone already believes in, the belief forms before the facts, and three failure modes repeat forever: you fully engineer quotes that were never winnable, you no-bid enquiries that a five-minute capability check would have revealed as easy wins, and you win orders whose real manufacturing cost surfaces later — in the shop, as margin erosion nobody priced.

Triage-with-evidence fixes the order of operations: facts first, effort second. The AI does the digging for every enquiry, in minutes — your estimators apply judgement to prepared facts instead of forming beliefs in the dark.

// Monday morning — three new RFQs, triaged before the first coffee

RFQ-2026-0311 · housing, milledQUOTE

Comfortable fit — and a cost reference exists

All tolerances within capability, margin ≥ 40% of tolerance band
Material 1.2312 — machined 214× in your history
Closest part: 94% similar, post-calc attached
Suggested action: quote from reference — est. 2 h effort

RFQ-2026-0312 · shaft, turned/groundQUOTE W/ DEVIATION

One characteristic at the edge — price it, don't absorb it

Ø 40 h5 over 480 mm: at grinding capability limit
History: similar feature caused 2 rework loops in 2024
Suggested action: quote with h6 alternative or priced risk premium
Evidence pack for the customer call: prepared

RFQ-2026-0313 · welded frame, XLNO-BID

Outside your envelope — decline in 5 minutes, not 5 days

Part exceeds largest machining envelope by 380 mm
Post-weld annealing required — no in-house process, sub-supplier margin ≤ 0
Suggested action: reasoned no-bid same day — goodwill intact
Engineer hours saved: ~3 days

Every verdict is a recommendation with evidence — the decision stays with your estimator. The point is not that the AI decides; it is that your people decide first thing Monday with facts that used to take all week.

How the check works — against your capabilities, not generic rules

Generic DFM checkers apply textbook rules. Yours is different, because the question is never “is this manufacturable” — it is “is this manufacturable by us, at margin“. That requires your data on both sides of the comparison, and it is exactly what we build:

01 — INGEST

The RFQ, as it arrives

2D drawings (PDF, scans)
STEP / native models
Specs & requirement lists
Email attachments included

02 — EXTRACT

The technical content, structured

Dimensions, tolerances, GD&T
Materials, surfaces, heat treatment
Quantities, deadlines, standards
Confidence-scored, source-linked

03 — CHECK

Against your capability profile

Machine envelopes & processes
Achievable tolerance tables
Material & treatment experience
Similar parts + post-calculations

04 — RECOMMEND

Verdict with evidence

Quote / deviate / no-bid
Cost frame from references
Risk flags, priced not absorbed
Estimator decides — always

When does the quotation check pay off?

The value scales with enquiry volume and the share of engineering in each quote. You are a candidate if most of these apply:

You handle hundreds of RFQs per year — machined parts, weldments, components or subassemblies to customer print.
Serious quotes cost days of engineering, and hit rates sit well below 50 percent.
No-bid decisions are made on gut feeling — and sometimes regretted in both directions.
Manufacturing feasibility problems surface after order intake, as rework and margin erosion.
Your part history and post-calculations exist in Teamcenter, SAP or archives — but nobody can search them at quoting speed.
Capacity is tight: you’d rather spend engineering hours on winnable bids and booked orders.

What the check is built on

Your data on both sides of the comparison — connected through the systems you already run.

AI drawing extraction (2D/PDF) STEP / model analysis Machine capability profiles Tolerance capability tables Geometric similarity search Post-calculation history Teamcenter & SAP integration Rework / NCR history signals On-prem / EU cloud

Before and after: the same week of enquiries

Fifteen RFQs land in a normal week. Here is how the week runs on each side of the change:

Before — first come, first served

Enquiries worked in arrival order — winners wait behind losers
Drawing review & feasibility per RFQ — 2–5 days each
"Have we made something like this?" — asking around, folder digging
Capability risks discovered in the shop — margin quietly eroded
5 of 15 quotes ship late or never — unquantified lost revenue

After — triage first, effort second

All 15 RFQs triaged with evidence — Monday, 9:00
Engineering hours flow to the winnable — references attached
Capability risks priced into the quote — or quoted as deviation
No-bids declined same day, with reasons — customers respect it
Same team, more quotes out, higher hit rate — measured, not hoped

Why inteliScience — and what this deliberately is not

Instant-quoting platforms exist — for sheet metal, for simple turned parts, for commodity machining. They work where parts are simple enough that a price can be computed from geometry alone. Your business is not that: machine building and complex components live on judgement about risk, capacity and customer relationships. So we don’t sell you a quoting robot. We sell your estimators a head start — the facts, gathered and checked, before their judgement begins. The verdict cards recommend; your people decide and sign every quote.

The second difference is whose rules the check runs on. Generic DFM tools apply textbook manufacturability rules; ours runs on your capability profile — machine envelopes, tolerance tables per process, material history, and the similarity search over your own parts and post-calculations in Teamcenter and SAP. Building that profile is real work (we do it together with your manufacturing and quotation people in the pilot), and it is precisely why the result is defensible in front of a customer where a generic checker is not.

And because RFQ documents are your customers’ IP under NDA, the pipeline runs on your infrastructure — on-premises or EU cloud. Nothing your customers send you is shipped to a public AI service.

When the open question is not manufacturability but which past projects and parts already solved a similar task, see our AI similar project search and our CAD similarity search.

Fixed price — no day rates, no surprises

Quotation Check Pilot

€16,900 fixed

Capability profile built for one manufacturing area — with your AV and quotation team
Extraction + triage run on 20 historical RFQs — validated in hindsight against what actually happened (won, lost, margin)
Similar-part matching demo on your Teamcenter / order history
Accuracy & hindsight report reviewed with your estimators
Rollout blueprint: live RFQ intake, Teamcenter/SAP integration, more areas

Start the pilot conversation

4 weeks · runs on your data, on your infrastructure · results are yours, whatever you decide afterwards.

For comparison

The cost of blind quoting

~70% of quotation engineering spent on bids that were never winnable
Won orders that erode margin because capability risk wasn't priced
And the invisible line: winnable enquiries that waited too long and went elsewhere

Frequently asked questions

Is this one of those instant-quoting portals?

No — and the difference matters for your business. Instant-quoting platforms compute prices from geometry for commodity parts; they collapse exactly where your margin lives: complex parts, judgement about risk, capacity and customer context. Our system is decision support for professionals: it gathers and checks the facts (feasibility, capability fit, references, cost frame), and your estimator makes the call. Nothing goes to a customer that a human hasn't decided.

Where does the "capability profile" come from — is that a big data project?

Smaller than you'd fear, and it's part of the pilot, not a precondition. Machine envelopes and process lists usually exist; achievable-tolerance tables are drafted per process in workshops with your AV and refined against real outcomes (the hindsight validation on 20 historical RFQs does much of the calibrating). The profile is versioned and yours — and it tends to become a valuable document in its own right, because most companies have never written down what they can actually do.

How good is the extraction from customer drawings?

Good enough to triage with, honest enough to trust: every extracted value carries a confidence score and a link to its spot on the drawing, low-confidence items are flagged, and the verdict shows which facts it rests on. In the pilot we measure extraction accuracy on your real RFQ documents — and because the triage is a recommendation, an extraction miss costs a correction, not a wrong quote.

Can we trust a no-bid recommendation?

You shouldn't trust it — you should check it, which takes two minutes because the evidence is attached: the envelope that's exceeded, the process you don't have, the reference that lost money. That's the design principle throughout: recommendations are cheap to verify and impossible to follow blindly. In hindsight validation, no-bid recommendations are tested against enquiries you actually worked and lost — so you see the would-have-saved hours before you rely on it.

Our RFQs are under customer NDA. Where does processing run?

On your infrastructure — on-premises or in an EU cloud under your control. Customer drawings and your post-calculations are exactly the two datasets that must never reach a public AI service, and the architecture treats both as such from day one.

How does this relate to your tender engineering offer for the energy sector?

Same family, different battlefield. Tender engineering (energy) digests 200-page utility specifications into compliance matrices — the pain is document mass and requirement deviation. The quotation check (this page) is built for machine builders and component makers: many smaller RFQs, and the pain is feasibility and cost against your own manufacturing reality. If your business sees both patterns, the two share infrastructure — extraction, similarity search, on-prem deployment — and we scope them together.

Your estimators' judgement is excellent. Give it facts instead of folders.

Send us three past RFQs — one you won, one you lost, one you regret — and we'll show you what the triage would have said, with evidence.

Talk to a Quotation AI Engineer

Always here to support you – reach out to us with your questions, and we’ll guide you every step of the way.

Phone: +40 774 463 429
Our Location: Str. Ploiești 9, 400157
Cluj-Napoca, Romania
Email: info@inteliscience.net
LinkedIn: inteliScience on LinkedIn
YouTube: inteliScience on YouTube