Nexus AI · For industrial sales teams
The customer sees one quote. You’re coordinating the checks, answers and approvals behind it.
The customer calls to check on an RFQ sent yesterday.
You know the request. You’ve read the attachment, checked the quantities and started the quote. Pricing is mostly ready. Two items need stock confirmation. Procurement is checking an alternative for a third. One line needs technical clarification.
“Can you send it today?”
You say you’re working on it. Then you send another round of internal follow-ups.
For a salesperson handling industrial RFQs, this is a familiar kind of pressure: being responsible for a response that depends on answers you don’t yet have.
For industrial sales, the RFQ is one place to examine where that time goes.
From the customer’s side, the task looks straightforward. Here are the items. Here are the quantities. What will they cost, and when can you deliver?
Inside your business, those questions take different routes.
The customer’s part number needs matching to your catalog. The price from the previous order needs checking against today’s cost. Stock exists, but some of it may already be allocated. An incoming purchase order could cover the shortage—if its arrival date fits the customer’s requirement.
You need the relevant answers before you can make a commitment.
So you move between the RFQ, the ERP, an old quote, a supplier email and a conversation with a colleague. Every answer moves the quote forward. Every unanswered question leaves another thread open.
Procurement has the supplier response. Engineering has the specification clarification. Your manager has approved the discount, subject to a margin check.
You have to bring those decisions together and make sure they still describe the same offer.
If the alternative part changes, does the price change too? If the quantity increases, is the stock confirmation still valid? If the supplier revises the lead time, who updates the delivery commitment?
A quote can look nearly finished while still depending on one unresolved detail. Until that detail is settled, you carry the uncertainty into every customer conversation.
Meanwhile, the next RFQ arrives.
A simplified example of the checks a sales rep may need to coordinate:
A revised quantity or alternative part can reopen checks that looked complete.
You start preparing for an account call, then receive a revised supplier price. You return to an open quote, then a customer asks for a different quantity. An approval comes through for the version you have just changed.
Each interruption requires you to reconstruct the situation: what was checked, what changed and what still needs confirming.
That work is difficult to see in a pipeline report. The report shows a quote in progress. It rarely shows the effort required to keep it moving.
By the end of the day, you may have handled dozens of messages without sending the response the customer is waiting for.
That is follow-up time alone, before product selection, quote preparation or customer conversations. It is a worked example, not an industry benchmark or a forecast of time Nexus AI will save. Measure your own RFQs to see the actual burden.
The instinct is to chase harder. Mark the email urgent. Call the colleague. Ask for an update.
That may get one quote out. The same dependencies return with the next enquiry.
What would help is a clear position on every RFQ:
With those answers together, you can make a specific request: “These two lines need a decision before we can send.”
You can also give the customer a useful update. Instead of “We’re still working on it,” you can explain what is confirmed and what remains open.
You still need to understand the customer’s priorities. Is delivery more important than price? Would they accept an alternative? Does this opportunity justify a commercial exception?
Those conversations are where your experience matters.
Routine information gathering should leave you with enough time and context to have them. Technical uncertainty needs an expert review. A pricing exception needs an approver. Both should reach the right person with the relevant facts already assembled.
That is the role Nexus AI is designed to support: extracting RFQ line items, bringing together stock, BOM and vendor checks, surfacing shortages and alternatives, and preparing the context for quote approval.
The salesperson can then focus attention on the items that need a decision and the customer conversation that follows.
Think about a recent RFQ that took longer than it should have.
Where did it wait? Which answer did you request twice? What had to be checked again after a revision?
Those details show where the quoting process needs to improve.
You own the customer relationship. You need a reliable way to see what your business can offer—and what is preventing you from offering it yet.