AI Quote Automation

What this looks like in practice

A buyer emails a request — sometimes structured, more often a sentence about what they need and a vague delivery date. A parsing layer reads the email, pulls out part numbers, quantities, and any pricing or delivery hints. It searches your catalog (Airtable, Sheets, ERP export, or PDF if that’s where it lives) and matches the items. It checks live stock if a connector exists, falls back to last-known if not. It drafts a quote in your house format — your line-item style, your terms, your sign-off.

The draft lands in the estimator’s inbox or a Slack channel. They review, edit anything that needs editing, and send. The build is designed to put a reviewable draft in front of a human in minutes, so when the buyer is also asking two competitors, your reply is queued up rather than waiting for someone to clear their inbox.

We build the connectors around what you already use. Outlook or Gmail for intake. Airtable, Google Sheets, NetSuite, or a pricing PDF for the catalog. DocuSign or Word for the final document. Make or n8n holds it together. The model only handles the parsing and the drafting — the rules, the prices, and the sign live with you.

Where this fits best

The economics work when there’s enough quote volume that turnaround time is costing real deals — typically 15+ requests a day, with deal sizes large enough that one or two saved per month pays for the build several times over. Industrial supply, fabrication shops, electrical and plumbing wholesalers, specialty distributors. If your team is already writing the same quotes from scratch each week, that’s the signal.

What this isn’t

This is not a replacement for a real CPQ tool if you already have one running well. It is not magic — if your catalog has gaps or your pricing rules conflict with each other, the build surfaces that before it papers over it. And we do not push quotes out the door without your team reading them first. The human-review gate is the point.