Quote automation that starts with your rules.

Quote automation is the production of a sales quote from an incoming request without a person assembling it by hand: the customer, the products, the pricing rules, and the terms come together into a draft that a person reviews. Sales quote automation applies the same idea inside a sales team — the request arrives, the system prices it against your rules, and the rep sends the result.

CPQ and quote generation software give you a rules engine and a data model your team configures and then maintains — typically a months-long implementation. We start from the rules instead: the pricing tiers, floors, discount authority, and approval windows your team already works by, encoded against the data you already keep. There is no new system of record to adopt, and the encoded rules are yours to keep.

Manufacturing quoting software prices from a configured catalog — parts, routings, machine times — inside its own data model. A permutation catalog resists configuration: hundreds of thousands of shape, packaging, and decoration combinations, so the setup project never ends and the desk keeps quoting by hand. We encode the rules your estimators already apply to the catalog and price history you keep — every permutation priced against your rules, exceptions to a person.

No. Price management software maintains list prices, discount grids, and rebate programs — inside another system your team then maintains. The tangle of discounts, rebates, and special deals is a rules problem: floors, discount authority, and exception thresholds, encoded against the customer and price data you already keep. If you already run one, the encoded rules read from it — nothing gets replaced.

Yes. The intake is usually unstructured — an email thread, a questionnaire, notes from a site walk — and reading it is the part AI handles well. Each request is read against your encoded rules, priced, and drafted; your team reviews the draft instead of building it. A questionnaire-to-quote flow is a normal starting shape.

It goes to an exception queue instead of to the customer. The quote is flagged with the rule it broke — a discount past the floor, a margin below threshold, an approval window that needs a signature — and a person decides. Nothing the rules would reject goes out unattended.

No. Reps keep working where they work today — the request comes in, the drafted quote comes back, and only exceptions ask for a decision. When the request is a formal RFP or RFQ rather than a quote, that is proposal automation — a different workflow, covered on our RFP-response page.

Quote to cash automation is the full chain — quote, order, invoice, cash. This page covers the quote. The order that follows is sales order processing, and the cash end is cash application — sibling workflows that run on the same encoded rules.

Yes — for service businesses, wage benchmarking is part of the pricing run. Before a bid is priced, AI agents go out and get rate information in the market the bid is in: what housekeepers, cleaners, and temps earn locally, plus federal and state minimum-wage changes — the heads-up that a minimum wage goes up a dollar in thirty days. The HR research step that takes a person a day or two happens in minutes, the hourly math a labor cost calculator does by hand is part of the same run, and the market wage data lands inside the price instead of a spreadsheet. Staffing agency pricing runs on the same encoded rules.

That is the shape of the engagement for walk-priced services. The site walk goes into a tablet — rooms, room types, condition, service line — and the draft price comes back while you are still on site, priced against your time standards per room type and your cost model: cost per occupied room or cost per clean. What the customer sees has already cleared your floors; anything that breaks a rule waits for a person instead of going out.

Janitorial bidding software — and the cleaning bid and janitorial pricing tools around it — is built for the simple case: one service, one pricing model, one building at a time. When cleaning services pricing spans three service lines, a dozen room types, and occupancy seasonality, the configuration project never ends and the desk keeps bidding by hand. We encode the rules your best estimator already applies — walk intake, market wage research, time standards, seasonality, the pricing exhibit for the contract — so every bid drafts itself and only exceptions reach a person. Commercial cleaning estimating software aims at the same seat; the difference is that the encoded rules are yours to keep.

CPQ for manufacturing models your products as configurations — options, variants, routings — inside the vendor’s data model, then prices by the seat after a months-long implementation. When customers order by the truckload, the price is rarely a configuration problem: it is the ERP price list, the customer’s contract terms, and what that customer paid last time. We encode those rules against the data you already keep, so the system drafts the quote and only the exceptions — a price below floor, a variant nobody has quoted before — reach a person.

Yes — that is the shape of the engagement at a global industrial manufacturer whose customers order by the truckload. Today each quote is an email answered from price files living in SAP; with the rules encoded, AI agents read the incoming request, match it to the ERP price list and the customer’s history — what they ordered, what they paid, the terms they hold — and draft the quote with your floors and approval rules already applied. A person reviews the draft instead of assembling it. Quoting software for manufacturers would have you rebuild that price logic inside its own catalog first; the encoded rules read the data where it already lives.