AI-Generated Quotes: How Automated Quoting Works

Using AI quote generation software means a system reads the request for quotation, extracts what matters, applies your pricing rules and delivers a proposal ready for review. The AI handles reading and writing; the price still comes from your rules and tables. Here is how the flow is organized and where the limits lie.

What data an RFQ carries

A request for quotation arrives in many forms: free-text email, a PDF item list, a customer spreadsheet or a short chat message. The challenge is that every customer writes differently. That is where language models help, because they understand unstructured text.

A simple five-block architecture

1. Input: a dedicated mailbox or messaging number that receives requests and attachments.
2. Extraction: the AI reads the content and returns structured fields such as customer, item, quantity, specification, deadline and delivery location.
3. Catalog matching: the system looks up each item in your catalog or bill of materials and flags what it could not find.
4. Pricing rules: an ordinary module, with no AI, calculates cost, margin, freight and taxes from your tables.
5. Proposal: the AI writes the text in your template, with lead times and terms, and the system produces a PDF for approval.

Keeping the AI separate from the calculation is the most important design decision. Numbers should come from auditable rules, not generated text.

How data extraction works in practice

You describe the fields you want and ask for results in a fixed format. For each field, the AI also indicates confidence or notes when the information does not appear in the request. Doubtful fields become questions to the customer instead of guesses.

A good test is to take 50 past requests, run the extraction and compare it with what the estimator recorded at the time. Accuracy per field shows where to adjust the instructions. Customer request data should follow your data-protection policy.

Where a human steps in

• Items missing from the catalog or with ambiguous specifications.
• Orders above a set value or from strategic customers.
• Non-standard commercial terms, such as special payment terms.
• Final approval before sending, at least during the first months.

Limits you need to know

• Complex technical drawings and tight-tolerance specs need human checking.
• If the catalog and tables are out of date, the proposal will be wrong, only faster.
• Highly custom jobs that depend on engineering cannot be priced from a table.
• The AI can misread a unit or a date; validating critical fields is mandatory.

How to get started

Choose a product family with predictable pricing, collect 50 real requests and list the required fields. Build the extraction and calculation for it, run it in parallel with your current process for four weeks and compare time and errors.

If you need help connecting request reading to your ERP, see our work in AI and automation. The first gain is usually response time, which often decides who wins the deal.

Frequently asked questions

Does the AI set the quote price?

Ideally not. Price should come from your own tables and rules, with the AI only reading the request and drafting the proposal. That keeps control and makes auditing easier.

Does it work with PDF and spreadsheet requests?

Yes, as long as the text is readable. Scanned PDFs need OCR and may require more checking.

Can quotes go straight to the customer?

It is possible for simple requests, but in the first months human review before sending is recommended.