Most quote requests arrive as email attachments, PDFs, or marked-up drawings, and an estimator has to manually re-key part numbers, quantities, and specs into the ERP before pricing can even start. While that data entry happens, the request sits, the customer waits, and faster shops win the job. AI quote request intake reads every inbound RFQ the moment it lands, extracts the part numbers, quantities, tolerances, and required dates, and opens a structured quote record in the ERP so the estimator starts with clean data instead of a cluttered inbox.

Why Quote Request Intake Matters for Manufacturing Companies

Most manufacturers run this process by hand, and it shows up as lost time and lost revenue. The recurring pain points:

RFQs arrive in inconsistent formats: PDFs, spreadsheets, scanned drawings, and email bodies
Estimators spend hours re-keying part numbers, quantities, and specs before they can price
Incomplete RFQs sit untouched because no one notices a missing material grade or quantity
No single view of which RFQs are still waiting on a first response

When intake is manual, the slowest part of quoting is data entry, not pricing. Every hour an RFQ waits in the queue is an hour a competitor uses to respond first and take the job.

How It Works

Here is the workflow most manufacturers use to automate quote request intake with AI.

Capture every RFQ channel into one trigger

Connect the sales inbox, web RFQ form, and EDI feed so each new quote request fires an n8n workflow the moment it arrives. Attachments, drawings, and email bodies all route into the same pipeline instead of waiting for an estimator to open them.

Extract the part and quantity data

An AI node reads the RFQ and pulls the fields an estimator needs: part numbers, revisions, quantities, material grades, tolerances, finishing, and required-by dates. It flags anything missing, like an absent material spec, so the gap surfaces immediately rather than mid-quote.

Open a structured quote in the ERP

The extracted data writes back to Epicor or NetSuite as a new quote record, linked to the customer account, so the estimator opens a populated quote instead of a blank screen and a pile of attachments.

Route by complexity and acknowledge the customer

Simple repeat parts route to a fast-track template; complex new parts go to a senior estimator. The workflow sends the customer an immediate acknowledgment with an expected quote date, so they know the request landed.

Tools Used in This Workflow

n8n - Orchestrates the RFQ intake workflow
Epicor or NetSuite ERP - System of record for the new quote
OpenAI or Anthropic - Extracts part and quantity data from RFQs
Salesforce or HubSpot - Holds the customer account and opportunity

Compliance and Regulatory Notes

Customer drawings and specs are often covered by NDAs. Run extraction on infrastructure the company controls and avoid sending proprietary drawings to third-party AI services without a data agreement in place.

Expected ROI

Estimated ROI

12 hours/week

Spent on quote request intake today

3 hours/week

After automation

$33,750

Capacity recovered per year

That is roughly 9 hours a week handed back to your team. At a blended rate of $75/hour for manufacturers, the recovered capacity is worth about $33,750 a year across 50 working weeks. Your real numbers depend on volume and rates; use this as a starting estimate, not a guarantee.