Four ways to configure. Built from real RFPs.
Every manufacturer's products fit one of four configuration modes — sometimes several at once. Each scenario below reflects requirements from a live RFP with a real EU or US manufacturer: who's in the loop, what the AI resolves on its own, and where a human or a site survey closes the gap.
One knowledge graph underneath all four modes.
The same infrastructure powers every scenario on this page — how variants and prices are modelled, how AI finds a match, and who gets a portal.
Build and maintain product variants directly in ServiceCPQ's knowledge graph — no ERP or SAP Variant Configurator required. Where SAP VC is already in place, ServiceCPQ can sit alongside it or replace it.
One pricing graph prices everything in the catalog — base product, aftermarket parts, accessories, recurring subscriptions, and engineer-to-order BOMs — instead of separate pricing logic per line-item type.
Submitted specs are mapped into a vector-based cluster space and matched to the nearest existing configuration or quote — for catalog and non-catalog items alike — with price attached.
Machine learning recommends the best price and discount for a given configuration and customer, instead of a flat price book or a rep's gut feel.
For make-to-measure catalogs and supplier-supplied items, AI finds and recommends the right supplier automatically, instead of an advisor searching manually.
One platform, one knowledge graph, six portals — supplier, installer, sales advisor, admin, product specialist, and customer self-service — each scoped to what that role needs to see and do.
Configure to Order
Guided flow for junior reps and distributors, expert mode for specialists, and customer self-service — one engine, three ways in, with a path to engineered exceptions when a standard build isn't enough.
Junior sales reps and distributors work a constrained, step-by-step flow — the system only ever offers valid next choices.
Experienced reps and product specialists get full manual control over the same underlying rules, for faster quoting on familiar ground.
B2B customers configure their own product using guided steps or natural language, then submit for internal approval or place the order directly, depending on their account's authority.
When a request needs a customized engineer-to-order option on top of a standard build — a modified load rating, an added attachment — it routes to engineering with the rest of the configuration pre-locked, so only the exception needs review.
Orders above a threshold, or containing customized options, route automatically to the right internal or dealer-level approver before confirmation.
Built to meet configure-to-order requirements submitted in a live RFP by a heavy-equipment manufacturer spanning mining, construction and road-construction equipment lines.
Frequently asked questions
Configure-to-order selects from a defined set of valid options. Make-to-order matches a request against known products with AI, closing gaps with a human review. Engineer-to-order starts from a customer's raw specification with no standard base, resolved partly by AI and partly by your engineers. Make-to-measure is a supplier-matched, dimension-driven configuration confirmed by a physical feasibility survey before the final price.
Each scenario on this page reflects requirements from a live RFP submitted by a real manufacturer in the EU or US.
Yes. All four modes run on the same underlying knowledge graph — the difference is how much of the configuration is pre-defined versus resolved by AI versus completed by a human, and where in the process the customer, an advisor, or an installer is in the loop.
It depends on the mode and the account's authority. Some customers can configure and order directly; others submit a configuration for internal or dealer approval first. Engineer-to-order and make-to-measure both include a human review or physical survey step by design, since those modes involve requirements that can't be fully resolved by rules alone.