What is an RFQ, and why does it recur?
An RFQ (request for quotation) is a request in which a buyer asks for a price on an already-defined need, backed by a specification. It recurs when a company regularly receives requests covering a stable catalog of products or services: same references, same conditions, only the context of the request changes. This repetition, on structured subject matter, is precisely what makes it automatable.
An RFQ differs from a tender in the broad sense: it does not ask you to argue for an offer, but to price a defined need. It is a shorter, more frequent, and more standardized response.
What makes an RFQ automatable, and what does not?
What makes an RFQ automatable is the stability of its subject matter: a defined catalog, explicit pricing rules, recurring conditions. The AI retrieves the requested reference, applies the rules in force, and assembles an answer consistent with previous requests. What cannot be automated is any pricing decision outside the standard scale: an exceptional discount, a borderline case, a negotiated condition commits the company and calls for human judgment.
| Element of an RFQ | What AI handles | What stays a decision |
|---|---|---|
| Requested reference | finding the product or service in question | fit with the customer's actual need |
| List price | applying the pricing rules in force | a discount or an off-scale price |
| Recurring conditions | reusing the standard conditions | a specific contractual commitment |
| Consistency | aligning the answer with past requests | a deliberate exception for a key customer |
How do you structure your data to automate RFQs?
Structuring your data to automate RFQs means making the catalog, the pricing rules, and the conditions explicit, so the answer can be assembled without judgment calls on each request. Data scattered across emails or isolated spreadsheets cannot be automated; data that is organized, dated, and authoritative can be. The automation effort is first and foremost an effort to put data in order, not to bolt a tool onto disorder.
On the Optivalue.ai platform, which publishes this site, these recurring requests are handled by mobilizing up to 100 sub-agents in parallel, which makes it possible to absorb a steady flow of requests without adding headcount for every peak.
The concession that clarifies everything
For a company that receives rare, always-particular price requests, each individually negotiated, automation gets no traction: the price is decided case by case. It gains value when requests cover a stable catalog, recur, and the time spent re-pricing the same reference becomes a burden in its own right. The dividing line is the stability of the subject matter: data that changes with every request cannot be automated.
The conditions for reliable RFQ automation
- An up-to-date catalog: the requested reference must exist and be dated.
- Explicit pricing rules: the price scale and its conditions are written down, not kept in someone's head.
- A record of every answer: the processed RFQ links back to the rules applied.
- A clear boundary on the scale: anything outside the scale is escalated to a human.
- Validation of sensitive cases: the price that commits the company gets signed off.
Frequently asked questions
Does automating RFQs mean setting prices automatically? Automating RFQs does not mean setting prices automatically: the AI applies a price scale and rules defined by the company, and any off-scale price remains a human decision.
Can an automated RFQ get the reference wrong? A poorly structured RFQ can lead to an answer on the wrong reference: that is why the quality of the catalog and validation of fit with the need remain decisive.
Do you need a perfect catalog before automating? An exhaustive catalog is not an absolute prerequisite, but the automatable share is limited to the references and rules that are actually structured; the rest still goes through a decision.
Do RFQs draw on the same library as tenders? RFQs mainly draw on a catalog and pricing rules, but they can share recurring technical answers with tenders, kept in the same library.
Automate your recurring price requests on your own data
Bring a real tender, an audit, or a security questionnaire. You will see the requirement-extraction coverage, the sources cited to the page, and the gap analysis on your own document, not a prepared demo.
Sources cited
- Presales practice: automation of recurring requests on structured data, boundary of the price scale (proprietary method, 2026).
- Specialized agent operating framework (publisher's brand documentation): up to 100 sub-agents mobilized in parallel to absorb a steady flow of requests.