What is AI quoting software?

What does it actually read?

The input is whatever an RFQ actually arrives as: a vector PDF or DXF drawing, a specification document, a scanned or photographed print, an Excel bill of materials, a points list or single-line diagram, or a forwarded email with all of the above attached. A tool built for this category is judged on how much of that mix it reads natively, not on how well it handles the one clean format a demo happened to use.

Where does the price come from?

This is the dividing line for the whole category. AI quoting software applies the shop's own labour standards, rate cards, material costs and margin rules to what it reads from the document in front of it. It carries no cost model of its own: change the shop's rates and the price changes with them, because the shop's numbers were always the ones doing the pricing.

How is this different from a generic AI tool reading a PDF?

A chatbot can summarise a drawing in prose. Quoting software has to do something stricter: extract a hole diameter, a bend count or a line-item quantity as a discrete, structured value, attach a confidence score to it, and link it back to the exact region of the source document it came from.

That traceability is what makes the output usable for a real quote rather than a plausible-sounding paragraph. Where geometry can be measured directly, a competent implementation measures it rather than asking a language model to estimate it from a picture.

Who is it built for?

Contract manufacturers with an RFQ-heavy sales process: fabricators, machine shops, panel and switchgear builders, and precision component manufacturers, where a meaningful share of incoming enquiries arrive as documents rather than a clean, pre-structured order.