How a Metalworking Shop Could Cut Quote Time with AI

This is a hypothetical scenario, built from typical situations, to show how AI for metal fabrication quoting can work in practice. The numbers are illustrative, not results from a real customer, and are meant as a reference for building your own estimates.

The scenario: a cut-and-bend shop with a quote backlog

Picture a fabrication shop with 35 employees making made-to-print sheet and tube parts. It receives 40 to 60 quote requests a week by email and messaging, nearly always with a PDF drawing, sometimes only a photo of a sketch.

Two estimators spend much of the day opening attachments, noting material, thickness, quantity and processes, checking price tables and writing the proposal. Average response time is two to three days, and smaller customers give up before then.

The real problem

• Around 70% of the time goes to repetitive reading and typing, not to pricing decisions.
• Incomplete requests force back-and-forth emails.
• Each estimator uses slightly different criteria for machine time and scrap.
• Small quotes get pushed back and miss the customer's decision window.

The solution, built in four stages

1. Reading the request: AI reads the email and attachments and extracts quantity, material, thickness, finish, deadline and customer details. If something essential is missing, it drafts a short question to the customer.
2. Reading the drawing: a vision model identifies main dimensions, holes, bends and part type on simple drawings. On complex ones, it just flags what it could not interpret.
3. Calculating the pre-quote: the system applies the shop's own price tables for sheet, cutting time, bending and finishing. The AI does not invent prices; it organizes data that feeds already approved rules.
4. Drafting the proposal: it generates the quote text in the company template, ready for review.

Where a human validates

The estimator remains the approver. They check the flagged dimensions, assess parts with tight tolerances or special welding, adjust margin and decide whether to take the job. Any request with a doubtful field lands in a manual review queue, and orders above a set value always go through a senior estimator.

Times before and after (illustrative)

• Simple part: from 25 to 40 minutes of work to 5 to 10 minutes of review.
• Medium part: from 1 to 2 hours to 20 to 40 minutes.
• Complex part: little change in time, but a draft with data already extracted saves typing.
• Customer response time: from two or three days to the same business day for a good share of requests.

Lessons from the scenario

First, the gain comes from removing reading and typing, not from replacing the estimator's judgment. Second, cost tables must be current, because the AI is only as good as the rules feeding it.

Third, start with one part type, such as simple bent sheet, and expand each month. To see how this kind of automation is structured, explore our automation and AI work. Before any project, measure your current time per quote; without that number there is no way to know the gain.

Frequently asked questions

Can AI read any technical drawing?

Not yet with the precision required. On simple, well-standardized drawings it works well, but complex parts need a human to check dimensions and tolerances.

Do I need to change my ERP to use this?

Not necessarily. You can start with a parallel system that reads requests, applies your tables and generates the draft, then integrate with the ERP later.