A request for quote, or RFQ, usually arrives with a deadline attached. A buyer sends a spec sheet to several suppliers and awards the order to the one that responds first with an accurate price. For a mid-size manufacturer, that puts the quoting desk under pressure every day.
The bottleneck is rarely a shortage of pricing knowledge. It is the time it takes one or two people to read a spec sheet, check current pricing rules, look up how a comparable job was priced before, and assemble a quote without a costly error. Mirai360 AI builds agents that take on that drafting work, while a person keeps final say over every quote that goes out.
Why does RFQ quoting slow down mid-size manufacturers?
A typical RFQ moves through several hands before it becomes a quote. Someone reads the spec sheet and checks it against what the shop can actually make. Someone else pulls current material and labor pricing, often from a spreadsheet or an Enterprise Resource Planning (ERP) system that only one person knows well. A third person checks whether a similar job was quoted before, because past pricing is often the fastest way to sanity-check a new number. Only after all three steps does anyone draft the quote itself.
Each handoff adds delay, and delay has a cost. A buyer comparing several suppliers often awards the order to whichever one responds first with a number that holds up. A slow quote risks more than one lost order. It signals to the buyer that the supplier may also be slow to deliver.
Manual quoting also carries a quiet error risk. A pricing rule that changed last month, a discount that should not apply to a rush order, or a unit conversion missed under deadline pressure. Any one of these can turn a won order into a loss on the shop floor. The person who catches the error, if anyone does, is usually the one filling out the invoice weeks later.
What does an AI agent do for RFQ and quote generation?
An AI agent is software built on a large language model that can read information, apply rules a business sets, and take action inside existing systems. Applied to quoting, an agent can:
- Read an incoming spec sheet and extract the parts, quantities, materials, and tolerances the buyer specified.
- Check current pricing rules for materials, labor, and any standing discount or surcharge that applies.
- Search past quotes for comparable jobs, so the new number is consistent with how the shop has priced similar work before.
- Draft the RFQ response in the shop's standard format, using the shop's standard terms.
- Route the draft to a person for approval before it is sent to the buyer.
The agent does the reading, checking, and drafting. A person still decides whether the quote is right before it leaves the building.
Why does a person still approve every quote?
A quote is a commitment. Once it is sent, the shop has told a buyer what a job costs and how long it will take. An agent that could send a quote on its own, with no review, would turn a drafting error into a signed commitment before anyone noticed the mistake.
Mirai360 AI builds the approval step into the workflow rather than treating it as an afterthought. The agent drafts. A person with pricing authority reviews the draft against the spec sheet and the pricing rules, then approves or corrects it before it goes out. This keeps the shop's pricing authority where it already sits, with the person accountable for it, while removing the manual work of assembling the draft in the first place.
How does Mirai360 apply its deployment discipline here?
Mirai360 AI does not put an agent into a full sales process on day one. The same discipline applies to RFQ and quote generation as to any other agentic workflow:
- Pick one narrow workflow first. The initial deployment covers quote drafting only, not the full sales cycle from lead to close. A narrow scope is easier to check and easier to fix.
- Record a baseline before deployment. Current quote turnaround time and current error rate are measured and written down before the agent goes live, so the shop has a real number to compare against later, not an impression.
- Keep a human approving every quote at the start. No quote reaches a buyer without a person reviewing it first, for as long as the shop wants that check in place.
- Set explicit authority boundaries. The agent works only from pricing sources and past-quote records the shop has approved, and every quote it drafts is logged, so there is a full audit trail of what the agent read and what it proposed.
- Scale only against pre-agreed numbers. The agent's role widens, such as sending routine quotes below a set value on its own, only when the recorded turnaround and error numbers support that change, and only by a decision the shop makes in advance rather than in the moment.
What does a mid-size manufacturer need to start?
A shop does not need a data science team to start. It needs a written description of how quoting works today, access to its pricing rules and past quotes, and a decision about where human approval sits. Mirai360 AI supplies the technical layer: the model access, the guardrails that keep the agent inside the pricing rules a shop defines, and the logging that makes every quote traceable. The work on the shop's side is describing its own process, not building software.
If quoting already runs through a shared inbox, a pricing spreadsheet, and an ERP or CRM system, an agent can typically connect to those tools directly. If the process lives mostly in one estimator's head, writing that process down is the first step, and it is worth doing even before any agent is involved.
How do you judge whether it is working?
Judge the deployment against the baseline recorded before it started, not against a general impression of whether things feel faster. Track the time from RFQ received to quote sent, the share of quotes that need a correction after drafting, and the share of RFQs answered before the buyer's deadline. Review these numbers on a set schedule, and widen the agent's role only when they support it.
A shop that follows this discipline treats the agent as an operational tool with a measured track record, not a one-time software purchase taken on faith.
Frequently asked questions
What does an AI agent for RFQ and quote generation actually do?
It reads an incoming spec sheet, checks current pricing rules, checks past quotes for comparable jobs, and drafts the RFQ response in the shop's standard format. A person reviews and approves the draft before it goes to the buyer.
Will the agent quote a price on its own, without review?
Not at the start. Mirai360 AI builds the deployment with a human approving every quote until the shop's own numbers support widening the agent's authority, and that widening happens only by a decision the shop makes in advance.
What does a manufacturer need before deploying this agent?
A written description of the current quoting process, access to pricing rules and past quotes, and a decision about where human approval sits. No in-house data science team is required.
How does a shop know if the agent is helping?
By comparing quote turnaround time, correction rate, and on-time response rate against a baseline recorded before the agent went live, reviewed on a set schedule.
Talk to us
Mirai360 AI works with manufacturers to scope a quote-generation agent against their own baseline numbers, before anything goes live. Book a 30-minute call: https://calendly.com/shivang-mirai360/30min
FAQ
- What does an AI agent for RFQ and quote generation actually do?
- It reads an incoming spec sheet, checks current pricing rules, checks past quotes for comparable jobs, and drafts the RFQ response. A person reviews and approves the draft before it goes to the buyer.
- Will the agent quote a price on its own, without review?
- Not at the start. Mirai360 AI keeps a human approving every quote until the shop's own numbers support widening the agent's authority, by a decision made in advance.
- What does a manufacturer need before deploying this agent?
- A written description of the current quoting process, access to pricing rules and past quotes, and a decision about where human approval sits. No data science team required.
- How does a shop know if the agent is helping?
- By comparing quote turnaround time, correction rate, and on-time response rate against a baseline recorded before the agent went live, reviewed on a set schedule.