What is B2B quoting with AI (and what it isn’t)?
B2B quoting with AI automates the repetitive stages of offering: reading a natural-language request, matching lines to the catalog, preparing a draft offer with prices and — optionally — supplier price requests for items without a fixed price. The rep still approves before sending.
It is not a chatbot that “invents” a price. Safe quoting splits roles: AI (or fuzzy matching) helps hit the item, but discount, margin and totals are calculated deterministically in code by your wholesaler’s rules. As a result the automation must never get the price wrong.
How does AI read the request and match catalog items?
The customer writes “ball valve DN50 PN16 brass, 10 pcs” or pastes an Excel table. The system extracts name, parameters and quantity, then searches catalog candidates — with a bonus for a hit manufacturer index and resilience to inflection, typos and missing diacritics.
The better the catalog data (indexes, categories, canonical parameters like DN/PN), the higher the hit rate. AI does not replace a tidy price list — it amplifies it. Without a clean catalog automation will guess; with a clean catalog it turns hours of write-up into minutes of approval.
Why must price be calculated in code, not by the model?
Language models handle language well, but they should not be the source of truth for price. 20% margin on 100 is always 125 — that’s arithmetic, not interpretation. If “AI quotes” really means the model guesses an amount, you risk errors on every offer.
The right split: item matching can be smart; price = f(purchase price, discount/margin rules, customer group). Same customer, same item, same rule → same price, regardless of who made the offer and when. That is the foundation of credible B2B AI quoting.
- Discount by group and specificity (name > category > manufacturer).
- Per-product pricing mode: discount or margin.
- “On request” items: purchase price from the manufacturer first, then margin.
- The rep approves the draft — AI does not send the offer unsupervised.
When does B2B quoting with AI start to pay off?
Payoff shows up at operational thresholds: above ~20–30 requests a day, a catalog of thousands of SKUs, several discount groups or frequent “on request” items. Then Excel and the inbox generate delays, inconsistent discounts and no case history.
OferIQ combines description matching, price calculation in code and the supplier RFQ loop in one flow with a PDF draft and case number. To see the “Excel stops being enough” threshold, see the comparison (/en/compare/oferiq-vs-excel/) and the quoting-automation guide (/en/guides/jak-zautomatyzowac-ofertowanie/).