What is AI estimating for manufacturing?

AI estimating uses AI to do the reading half of quoting: ingesting the RFQ package as it arrives, 2D drawings, scanned prints, spec documents, email threads, extracting and structuring every requirement, screening feasibility against the shop's capabilities, and drafting the quote from the shop's existing pricing logic and quote history. The estimator reviews and owns the number; the AI removes the hours of reading, retyping, and data-chasing in front of it.

Does AI estimating invent prices?

Not in Mavlon's design. Your rates, margins, rules, and history are the pricing logic; Mavlon structures the RFQ and applies that logic. Shops that want a physics-based should-cost benchmark computed from 3D CAD are describing a different tool category, simulation platforms like aPriori, which answer what a part should cost rather than what your shop will quote.

What does AI estimating do when data is missing from the RFQ?

It turns gaps into questions on day one. Ambiguous requirements are flagged rather than guessed, and missing information becomes a concrete list to send back to the customer immediately, instead of a discovery on day four that restarts the quote.

Is AI estimating proven in production?

Montanstahl, a Swiss special-steel manufacturer, deployed Mavlon across three plants to screen RFQ feasibility, with the engine graded at 85%+ accuracy by their own engineers. The full case study is public, with named metrics.

AI Estimating for Custom Manufacturing

Your pricing logic wins the work. The reading is what eats the week.

AI that reads the RFQ package, structures every requirement, screens feasibility, and drafts the quote, in your logic, not ours.

By Atishay Jain, founder of Mavlon · Updated July 2026

The problem, precisely

Quoting is two jobs. Only one of them is pricing.

Ask an estimator where the day goes. It is not the arithmetic. It is reading: opening eleven attachments, extracting materials and tolerances and quantities from drawings, reconciling the spec PDF against the note on page two, discovering the missing datum on day four, retyping all of it into a spreadsheet or a system. The pricing, the part your shop is actually good at, gets the leftover minutes.

Most quoting software automates the second job and assumes the first one is done. Simulation platforms want a 3D model. Geometry platforms want clean CAD files. ERPs and CPQs want structured records. The RFQ you actually received is none of those things.

Reads the real package

2D PDFs, scanned prints, spec documents, quantity tables, email threads. Every requirement extracted and structured: parts, materials, tolerances, finishes, quantities, standards, special notes. Ambiguity flagged, never guessed.

Screens feasibility first

Before hours are spent: can your shop make this, should your shop quote this? Capabilities, size limits, process fit, your no-quote rules, applied on day one. The most expensive quote is the one you should never have written.

Drafts the quote in your logic

Your rates, margins, and rules, informed by your history of similar parts. Mavlon invents nothing: the number that comes out is your number, drafted for your estimator to review and own.

How it runs

From inbox to reviewable quote

Forward the RFQ package. Drawings, specs, the email thread, as-is. No 3D model, no data prep.

AI reads everything and returns a structured requirement record, with every extraction traceable back to where it came from on the page.

Feasibility verdict: quote it, decline it, or ask first, with the missing-data questions drafted for the customer.

The quote drafts itself from your logic. Your estimator reviews the flagged items and owns the final number, in minutes of review instead of hours of assembly.

Answer every RFQ instead of triaging. Win rate follows response time, and response time follows the reading.

Proven in production

Not a concept. A system running across three plants.

Case study · Montanstahl

Montanstahl, the Swiss special-steel manufacturer, ran every RFQ through the same bottleneck: senior estimators answering “can we make this?” drawing by drawing, across plants in Switzerland, Germany, and Italy. Mavlon now screens RFQ feasibility in production, embedded in their existing workflow.

>85%

Accuracy

Seconds

vs. hours

1000s

Hours saved

7-fig

Annual upside (est)

Read the full case study →

Every estimating tool wants to own your pricing. We built the opposite: your logic is the asset, and it already wins your work. What should be automated is the reading, the structuring, and the chasing that stand between an RFQ and that logic. That is what Mavlon does, and nothing else.

Bring your messiest RFQ. Watch it become a quote-ready record.

Real drawings, real specs, the actual email thread. 15 minutes, live.

Book a Demo

15 minutes · We run your hardest RFQ live

Frequently asked questions

What is AI estimating?

AI doing the reading half of quoting: ingesting the unstructured RFQ package, structuring every requirement, screening feasibility, and drafting the quote from your existing pricing logic. The estimator reviews and owns the number; the hours of reading and retyping disappear.

Does it invent the price?

No. Mavlon carries no cost models. Your rates, margins, rules, and quote history are the logic; Mavlon applies them. If you want a simulated should-cost benchmark from 3D CAD, that is a different category, see our honest Mavlon vs aPriori comparison.

What happens when the RFQ is missing information?

Gaps become customer questions on day one: a concrete drafted list, not a day-four discovery. Ambiguous requirements are flagged for your estimator rather than silently guessed.

We run a CPQ. Does this replace it?

No, it feeds it. The structured record lands in your CPQ as the clean input it was always waiting for. See From RFQ Inbox to Clean CPQ Record.