Do I need technical staff to run an AI quoting agent?

No. A platform approach means the technical components — model access, guardrails, logging, and cost controls — are provided for you. Your role is to define the rules: which prices apply, which discounts are allowed, and which quotes need human approval. Mirai360, for example, offers a managed option where the platform team operates the system and your team only reviews the agent's output.

Will the agent send wrong prices to customers?

The agent reads prices only from sources you approve, and guardrails block the agent from inventing figures. For additional safety, you can require human approval on every quote at the start, then relax that requirement for routine, low-value quotes once the agent has proven reliable in your own records.

How is an agent different from the email templates I already use?

A template still requires a person to notice the enquiry, look up the prices, fill in the details, and remember the follow-up. An agent performs those steps itself and only involves a person at the approval points you define. The template saves typing; the agent saves the whole workflow.

What does a project like this cost?

The cost depends on enquiry volume, the systems the agent must connect to, and whether you self-host, use a managed service, or commission a custom build. Rather than trusting a generic figure, ask a provider for a quote based on your actual volumes, and compare that quote against the value of the orders you currently lose to slow responses and missed follow-ups.

AI agents for quotes and follow-ups: the fastest return for an SME

For most Small and Medium-sized Enterprises (SMEs), the fastest return from Artificial Intelligence (AI) does not come from a chatbot on the website. The fastest return comes from putting AI agents to work on quotes and follow-ups, because quoting and follow-up work is repetitive, rule-bound, and directly tied to revenue. An enquiry that receives a quote quickly, and a quote that receives a timely follow-up, is more likely to become an order.

Why quotes and follow-ups leak revenue

Consider how a typical enquiry moves through an SME. A prospect sends an email or fills in a form. Someone in the office reads the enquiry, looks up prices, checks stock or capacity, drafts a quote, and sends the quote back. If the prospect does not reply, someone must remember to follow up. Each of those steps depends on a person being available, and each step adds delay.

The delay is where revenue leaks. Prospects often send the same enquiry to several suppliers, and the supplier who responds first with a clear, accurate quote holds an advantage. Follow-ups leak revenue in a quieter way: a quote that nobody chases simply expires. The owner rarely sees this loss on any report, because a quote that dies of silence leaves no record of failure.

What an AI agent actually does here

An AI agent is software built around a Large Language Model (LLM) that can read information, make decisions within rules you set, and take actions in your existing systems. An agent differs from a chatbot in one important way: a chatbot only talks, while an agent completes work.

Applied to quoting, an agent can do the following:

Read an incoming enquiry from email or a web form and extract the products, quantities, and delivery requirements the prospect described.
Look up current prices and availability in your price list, spreadsheet, or Enterprise Resource Planning (ERP) system.
Draft a quote in your standard format, using your terms and your wording.
Route the draft to a person for approval when the value or the discount exceeds a threshold you define, and send the quote directly when the request falls inside normal rules.
Record the quote in your Customer Relationship Management (CRM) system or tracking sheet.

Applied to follow-ups, the same agent can watch the list of open quotes, send a polite follow-up message at intervals you choose, escalate to a salesperson when a prospect replies with a question, and mark quotes as won, lost, or expired so your pipeline stays honest.

The rules stay yours

A reasonable worry is that an agent will promise a discount you never approved or quote a price from an outdated list. A well-built agent platform addresses this worry with guardrails: explicit limits on what the agent may say and do. You define the price sources the agent may read, the discount range the agent may offer, and the situations that must go to a person. Every message the agent sends is logged, so you can review what was said to whom.

This design matters more than the intelligence of the underlying model. The value of an agent for an SME is not clever conversation; the value is consistent execution of your own rules, at any hour, without fatigue.

Why this is the fastest return

Three properties make quoting and follow-up the natural first project.

First, the work is well-defined. Your business already has prices, terms, and a quote format, so the agent has clear material to work from and clear rules to follow.

Second, the result is measurable. You can count quotes sent, measure the time from enquiry to quote, and count follow-ups completed, before and after the agent starts. You do not need a consultant to tell you whether the change worked.

Third, the risk is contained. A quote is a document a person can review before it goes out. You can start with the agent drafting and a person approving every quote, then widen the agent's authority as trust builds.

What you need to start

You do not need a data science team. You need three things: a written description of how quoting works today, access to your price information, and a decision about where human approval sits. Platforms such as Mirai360 supply the technical layer — the LLM gateway that connects to models, the guardrails, the cost controls, and the dashboards — so the work on your side is describing your process, not building software.

If your business runs on common tools such as shared inboxes, spreadsheets, and a CRM, an agent can usually connect to those tools directly. If your process lives in one person's head, the first step is writing that process down; the exercise is valuable even before any agent exists.

How to judge success

Set expectations before the agent goes live, and measure against them. Useful measures include the time from enquiry to quote, the share of quotes that receive at least one follow-up, and the share of quotes that reach a recorded outcome instead of going silent. Avoid judging the project on vague impressions; the numbers from your own systems are the only evidence that matters.

Start small, review weekly, and expand the agent's scope only when the numbers support the expansion. An SME that follows this discipline treats AI as an operational tool, which is exactly what AI should be.

FAQ

Do I need technical staff to run an AI quoting agent?: No. A platform approach means the technical components — model access, guardrails, logging, and cost controls — are provided for you. Your role is to define the rules: which prices apply, which discounts are allowed, and which quotes need human approval. Mirai360, for example, offers a managed option where the platform team operates the system and your team only reviews the agent's output.
Will the agent send wrong prices to customers?: The agent reads prices only from sources you approve, and guardrails block the agent from inventing figures. For additional safety, you can require human approval on every quote at the start, then relax that requirement for routine, low-value quotes once the agent has proven reliable in your own records.
How is an agent different from the email templates I already use?: A template still requires a person to notice the enquiry, look up the prices, fill in the details, and remember the follow-up. An agent performs those steps itself and only involves a person at the approval points you define. The template saves typing; the agent saves the whole workflow.
What does a project like this cost?: The cost depends on enquiry volume, the systems the agent must connect to, and whether you self-host, use a managed service, or commission a custom build. Rather than trusting a generic figure, ask a provider for a quote based on your actual volumes, and compare that quote against the value of the orders you currently lose to slow responses and missed follow-ups.