Can an AI agent send quotes entirely on its own?

Much is technically possible, but we advise against it. You approve prices, discounts, and terms before a quote goes out. The agent prepares the work and sends only after your approval. That keeps a human check on mistakes and leaves responsibility with your team.

What does the agent need to prepare quotes?

Your pricing rules, including prices, discounts, and terms; a set of recent quotes as references; and a connection to the system holding your customer information, such as your CRM. Without that foundation, the agent can generate text but not a reliable proposal.

Can this work alongside my existing quoting software?

Often, yes. The agent does not have to replace your quoting software. It can read requests, organize information, and prepare a draft in your existing environment. What is possible depends on the integrations your software provides.

What does quote automation cost?

That depends on your quoting process, systems, and request volume. Voltti provides a custom quote after a pilot, rather than charging a fixed monthly fee.

How soon will this be useful?

A pilot covers one type of request over a short period. Afterward, you can assess how much preparation the agent handles and which checks remain with you. Expand to follow-ups and more request types only once the first workflow is stable.

Automating quotes with an AI agent: how it works

A quote left waiting for three days can lose momentum. Responding promptly with a sound proposal gives the customer something useful to compare. Yet your team repeats the same work every week: read the request, find the details, check prices, write, review, and send. An AI agent can prepare much of that work. It does not replace your judgment; it handles the groundwork by reading the request, consulting your CRM, drafting a quote, and putting everything in place for your final check. This article explains that quoting process, what the agent does, and where you remain in control.

Illustration of an AI agent preparing quotes around a cobalt document pavilion, with a human review workstation beside it — The agent prepares the quote; you check the price and terms.

Why quotes are a useful candidate for automation

Quotes involve predictable work with unpredictable input. Every request differs slightly: a different combination of products, a different customer, or a different starting point. The steps that follow are usually similar. That can make them suitable for automation: enough repetition to justify the work, but enough variation that a fixed script alone may fall short.

Response time matters. The often-cited study The Short Life of Online Sales Leads in Harvard Business Review (2011) reported that only 37 percent of the companies studied responded to an online inquiry within an hour. In a separate analysis discussed in the article, firms that tried to contact a lead within that hour were nearly seven times as likely to qualify it as firms that waited even an hour longer. The research is old and concerns lead qualification, not a guaranteed increase in accepted quotes. It supports taking response delays seriously, not rushing an unchecked proposal to a customer.

There are practical failure points too. Quotes sit unanswered because something else is more urgent. Prices get copied incorrectly under time pressure. Follow-ups are missed because nobody tracks expiring quotes. A well-configured agent can track open quotes and prepare requests through a defined sequence. Checks are still necessary: outdated prices, incomplete data, or a failed integration can cause errors.

Creating a quote with AI: what the agent prepares

Creating a quote with AI does not mean giving a text generator a prompt and hoping for the best. An agent uses your own business data and follows a defined preparation process:

read and organize the request: what the customer wants, which products or services are relevant, and what information is missing
retrieve customer details from your CRM or address book, including earlier quotes and agreements
draft a quote using your price list and the wording in your existing templates

The order only works if the agent first has your pricing rules. An agent writing quotes without defined prices, discount rules, and conditions can produce fluent copy with incorrect amounts. Start by recording which prices and terms may appear in your quotes, not by generating text. That groundwork gives the agent a usable basis, but it does not replace checking each draft.

The connection to your systems needs attention too. A good draft helps less if someone still has to retype all the customer details afterward. For advice on useful integrations and ones to avoid, read our article on connecting AI to your CRM and other systems.

Step in the quoting process	What the agent does	What you keep doing
A request arrives	Reads the email or form, summarizes the request, and flags missing information	Decide whether the lead is worth pursuing
Retrieve information	Finds the customer in your CRM and reads earlier quotes and agreements	Bring your understanding of the relationship and context
Draft the quote	Prepares the proposal using your price list and templates	Check the details, tone, and personal touch
Check the price and terms	Calculates amounts using your rules and prepares them for approval	Carry out the final check and approve
Send	Sends only after your approval, at the agreed time	Give the go-ahead
Follow up	Monitors the validity date and flags a quote that is about to expire	Decide on an offer or the next step

The quoting process with an AI agent, step by step

The table summarizes it. Here is how the process works in practice.

A request arrives by email, through your website, or in a short note someone takes during a phone call. The agent reads it, identifies the type of quote needed, and checks whether all the information is present. If something is missing, it drafts specific questions or creates a task for the colleague who knows the customer.

Next comes context. The agent finds the customer in your CRM, reads earlier quotes and notes, and includes the pricing rules that apply to the relationship. Those previous conversations and agreements help turn a generic proposal into a quote that reflects what you know about the customer.

The agent then writes the draft, combining the products, prices, and wording from your templates to fit the request. The draft goes into your quotes folder or CRM, with a short summary at the top for the reviewer.

You do the check. Is the price correct? Is the discount within the permitted range? Are the terms what you intended? Human oversight stays here: the agent has done the preparation, but you sign off on the proposal carrying your name. After your approval, the agent sends the quote at the agreed time and records the send in your systems.

Finally, the agent tracks open quotes and their validity dates. If a date passes without a reply, you receive an alert with a suggested next step: a short reminder, an alternative, or a polite closing message. The agent does not pressure customers; it maintains the overview that might otherwise get lost between two mailboxes.

Automating quotes without losing control

When automating quotes, decide where to draw the line. Our starting point is that the agent prepares and a person decides. You approve prices, discounts, and terms before anything goes out. An incorrect amount in a polished quote can outweigh the benefit of preparing it faster.

The work of our own Hermes installation at Voltti also starts with a clearly defined task. The agent carries it out and records what happened. In our CMS, it sometimes edits and publishes directly; we check the result afterward and make corrections. For quotes, we choose a different boundary: a person approves the price and terms before sending.

The controls behind that boundary are straightforward. Do not give the agent a sending path that bypasses approval, retain each version of a quote, and require a person to review an unusual price. That lets you delegate preparation while retaining control of consequential decisions. The workflow must enforce those rules; a written instruction alone is not an access control.

How to start small with quote automation

Start with one type of request that comes back often enough, rather than your entire quoting process. Choose the workflow that causes the most frustration, often standard requests that already follow a set pattern. Document the pricing rules, gather around ten recent quotes as references, and spend a few weeks having the agent create drafts that a person always checks. Then consider expanding to follow-ups and other request types.

We explain that approach, one workflow before expanding, in automating business processes step by step. Our page on AI automation for your business explains which tasks suit fixed steps and which need a flexible approach.

Want to see what this could look like for your team? Voltti's approach explains how a pilot works: one task, a defined period, and a clear point where you have the final say. For the broader setup, explore Voltti's AI agent solution. Discuss your pilot and we can review your quoting workflow together, for businesses in Belgium and the Netherlands.