After an RFQ arrives organize requirements, attachments and open questions

Key takeaways

• Preserve the original RFQ and attachments before generating an answer
• Keep engineering search results traceable to their sources
• Require human confirmation before price, lead-time, or external-send commitments

Check where your RFQs come from

RFQs may arrive through email, forms, phone calls or an existing system. This article focuses on Aurora’s demonstrated email-and-attachment workflow. For other channels, establish how data will be obtained before choosing an intake method.

This is a product workflow example, not a requirement for every company. If an existing system already retains complete requirements, identify repeated entry or clarification work before evaluating AI assistance.

Example: what should one incoming RFQ produce?

This is a hypothetical teaching example, not a customer case or measured result. An email requests 100 units of AB-001 and includes AB-001-R2.dwg, but gives no material or delivery date. The first output should be a review checklist, not a price.

Known: drawing AB-001, quantity 100 and an attachment labelled R2. Open questions: material, surface treatment, requested delivery date and whether R2 is the approved revision. Link extracted fields to their source; the filename does not replace title-block and approval checks.

Sales clarifies commercial requirements and engineering confirms the drawing and process before a reply is approved. This checklist can test missing-field detection before an ERP integration.

Start with a bounded pilot

Select one RFQ category and prepare de-identified emails, attachments and human-reviewed answers, including missing files, ambiguous revisions and absent quantities. Record receipt, intake completion, engineering review and reply completion.

Measure active handling time separately from waiting time. Waiting for a customer reply is not automatically time saved by AI. Verify that unknown requirements stay unknown before comparing speed.

The time-consuming part of an RFQ is rebuilding the requirement

A manufacturing RFQ often begins with email, but producing a useful quotation requires more than the message body. Sales must identify the customer, part number, quantity, material, finish, lead-time request, and attachments. Engineering then searches historical drawings, checks revisions and manufacturability, and asks for missing information when necessary.

When email, DWG files, spreadsheets, historical drawings, and discussion records are distributed across folders and people, every handoff repeats the same reading. The first goal of RFQ automation is not instant pricing. It is to keep the original request, attachments, search evidence, and processing state in one task.

First, turn the email and attachments into a traceable task

The task should retain the message, attachment list, received time, and responsible person, then organize identifiable fields such as part number, quantity, specification, and requested delivery date for review. The system can flag missing fields, but it should not invent terms that the customer never supplied.

Aurora has a working product screen that shows RFQ email and attachments entering a task, preserving context, and continuing into search. Acceptance at this stage should verify source completeness, whether extracted information can be checked against the original, and whether the task can resume after the user leaves the page.

Next, connect engineering search and technical review to the same task

Drawing numbers, dimensions, and product descriptions in the RFQ can become search conditions. The system can present candidate drawings and related records together, while an engineer decides which drawing, revision, or historical record is relevant. No result, multiple candidates, and unclear revisions must remain explicit review states.

The task should retain the search conditions, candidate results, and the person's selection so sales can see what evidence supports the engineering decision. If inventory, routing, or cost information is also required, list the systems and responsible roles here instead of assuming every source is already integrated.

Draft the response, but confirm price, lead time, and external sending

Once the requirement and engineering evidence are organized, AI can prepare a response draft, list missing questions, or summarize the next step. Unit price, discounts, committed lead time, material substitutions, and manufacturability still carry commercial or engineering responsibility and should not be sent merely because the prose looks complete.

Before an external send or a formal quotation write, the workflow should show cited sources, primary conditions, and unresolved exceptions to an authorized reviewer. Aurora's role is to let drafting, search, and human decisions share the same task context—not to replace the company's quotation accountability.

Measure four outcomes instead of judging the model's answer

Measure the time from RFQ receipt to a complete review packet, the number of sales-engineering handoffs needed to fill gaps, whether candidate drawings and final selections can be traced to sources, and whether missing items, exceptions, and the approver are visible before sending.

Start with 20 to 50 de-identified historical RFQs containing complete requests, missing files, inconsistent drawing numbers, and multiple revisions. The workflow is ready to expand only when normal and exceptional cases can both be identified, handed off, and resumed.

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