Automating Quotes in Machine Building: From Spec to Proposal Draft in Hours

Table of contents

1. Why quotes in machine building take so much time
2. Which steps of quoting can be automated
3. Special requests: check first, then design
4. What stays with people
5. Quoting today vs. with AI
6. For bid and proposal management too
7. Not distributor-style RFQ automation
8. From practice
9. FAQ

Why quotes in machine building take so much time

A quote for a machine or plant is not a catalogue extract. It describes a technical solution to a specific need, and that solution has to be buildable, verifiable and correctly priced. How quoting, configuration and CPQ connect into one flow is explained in the guide to CPQ with AI.

Whether it is a tender or a 50-page specification, most of the answers already exist, scattered across old proposals, data sheets and specifications. The effort arises in three places:

• The request is incomplete. Customers describe their need, not their configuration. Values are missing, standards are not named, constraints sit between the lines.
• The knowledge is scattered. Data sheets, option lists, price lists and earlier proposals live in different systems, often in different versions.
• The experience is scarce. Only a few people in application and project engineering know which configuration has worked in similar cases. Every question to them lengthens the turnaround.

This is no edge case. Reps already sell only around 28–30 % of their time, and roughly 31 % (about twelve hours a week) goes to finding and creating content alone. By the time your proposal is ready, a competitor has often already delivered.

Which steps of quoting can be automated

Writing a proposal with AI does not mean having a text generated. It means automating the preparatory steps that cost the most time today:

1. Evaluate the request. The spec, tender, email or call notes are read and the requirements structured, even with customer-specific abbreviations and terminology. Missing information is turned into a question.
2. Propose product and configuration. The requirements are checked against data sheets, option data and validity rules. The result is a buildable proposal with a justification.
3. Reuse proven content. Descriptions, technical explanations, arguments and references are taken from won proposals for similar cases and adapted.
4. Create the draft proposal. Everything flows into a draft in the company template, with prices from the price lists. Every technical statement points to its source.
5. Check against specification and contract. Before approval, the draft is laid against the specification, contract terms and factory standards. The experts review a handful of findings instead of hundreds of pages: a penalty clause that is not priced, a factory standard that is not met, an acceptance test missing from the scope, a price below similar projects.

Because the right product is found first and the proposal is built second, the draft is not only fast but technically sound. In Genow, this happens in one chat: the workspace decides which specialist takes the request, and nobody has to switch between tools. Read more about AI-assisted proposal writing on its own page.

Special requests: check first, then design

In machine building, few things cost as much time as the special request. It lands in design engineering, which starts from scratch, and weeks pass before the quote. Yet something very similar has often been built already, just for another customer and in an archive nobody searches.

An example: the request is for a stainless steel housing to IP67, flange DN 80, PN 16, for temperatures up to 180 °C. A few years ago almost the same thing was built for another customer, rated up to 200 °C even. The drawing, bill of materials and price exist; nobody finds them.

So every special request is checked before it reaches design engineering. There are three possible outcomes:

• A standard product fits. The quote comes straight from the catalogue or the modular system.
• It has been built before. Drawing, bill of materials, price and lessons from the earlier order are at hand and get adapted.
• It is genuinely new. Then the request goes to design engineering, with a complete brief instead of a forwarded email.

That way, only genuinely new work reaches design engineering.

What stays with people

Automated quoting does not replace sales. It shifts the work from searching to deciding.

• Price and terms. Pre-calculation, discounts, payment terms and strategic prices are decisions, not research. They stay with sales or run through the rules in the CPQ.
• The customer relationship. Which solution this customer needs right now, what was said in the last conversation and what matters to them is what people bring.
• Approval. A quote is binding. The AI delivers a draft; sales reviews and approves.

Quoting today vs. with AI

Step	Today	With AI
Capturing the spec	Read by hand	Automatically structured, gaps turned into questions
Finding answers	Days searching old proposals	Matched from proposal history and product knowledge
Choosing the product	From memory, often the familiar one	Buildable, justified, checked against rules
Special request	Straight to design engineering, from scratch	Checked first: standard, built before or genuinely new
Draft proposal	Start from zero in the template	Complete in the company template, with sources
Review	Spot check before sending	Every requirement and every contract clause matched
Role of sales	Searching and assembling	Reviewing, deciding, approving

For bid and proposal management too

The same principle helps the teams who write proposals and tender responses full time. Connected to the history of earlier proposals, the draft is built together with the author and refined step by step, instead of starting from a blank page every time. What won before gets reused. How long tenders are answered in detail is covered in How to write an RFP response.

Not distributor-style RFQ automation

The distinction matters: this is not the RFQ automation distributors use. Those tools read resolved part lists and price them into the ERP. Manufacturers of configurable products first have to determine the right product from their own portfolio. The difference lies in the job, not in the document format.

From practice

The KION Group, home to brands such as Linde and STILL, uses Genow with more than 2,000 active users worldwide. Configuration and quote preparation run twice as fast as before. The reason: answers to technical questions are ready in seconds, each with the source it came from. Read the full story in the KION case study.

FAQ