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Configuration

Configure-to-Order Quoting

Sales teams configure complex products against live product logic while a validation gate stops invalid configurations from ever reaching a quote.

Rule EngineConfiguratorData Tables & DQL

The problem

Complex products carry compatibility logic that lives in spreadsheets, PDFs, and the heads of senior engineers. When sales quotes without that logic, invalid configurations slip through, orders bounce back from engineering, and delivery dates slip. The cost shows up as rework, credibility loss, and quotes that stall while engineering re-checks every line.

How it works in Neblex CPQ

Neblex CPQ captures product logic in eight typed rule kinds: visibility, violation, recommendation, pricing, lead time, BOM, defaulting, and computation. Rule actions can hide options, require fields, disable or allow choices, set values, recommend alternatives, show messages, raise violations, and adjust price and lead time. The configurator presents it all in tabbed sections built from layout templates, so reps work through a structured screen instead of a flat option list.

The rule test panel shows a per-condition evaluation trace, so admins can see exactly why a rule fired. Data-driven rules query data tables and master data through DQL, and sandboxed JavaScript functions handle logic that goes beyond declarative rules. Rule conflict detection and where-used dependency analysis protect the model as it grows.

The validation gate is the enforcement point: a configuration cannot be committed while violations, missing required fields, or conflicts remain. Configuration analytics then report funnel drop-off, option pick rates, and time-to-quote so admins keep improving the model. Request a demo to walk through the configurator with your own product logic.

Step by step

1

Model the product

Define options and structure the configurator with tabbed sections from layout templates.

2

Author typed rules

Capture engineering logic as visibility, violation, defaulting, computation, pricing, lead time, and BOM rules.

3

Test with the trace

Use the rule test panel and its per-condition evaluation trace to verify behavior before release.

4

Configure and validate

Reps configure against live rules while the validation gate blocks commit until violations and required fields are resolved.

5

Commit and quote

Committed configurations flow onto the quote with pricing and lead time already applied.

6

Improve with analytics

Configuration analytics surface funnel drop-off, option pick rates, and time-to-quote.

Platform capabilities used

  • 8 typed rule kinds
  • Validation gate on commit
  • Data tables and DQL
  • Per-condition evaluation trace
  • Rule conflict detection
  • Where-used dependency analysis

Common questions

How does the validation gate work?

The gate blocks committing a configuration while violations, missing required fields, or conflicts remain. Reps see exactly what is unresolved, and only a clean configuration reaches the quote.

Can rules use data from outside the model?

Yes. Data-driven rules query data tables and master data through DQL, and sandboxed JavaScript functions can call named external API endpoints with rate limits and hardened HTTP.

How do we change rules without breaking existing behavior?

Where-used dependency analysis shows what a change touches before you make it, and rule conflict detection flags contradictory rules. Rule and function version history keeps a record of every revision.

See it on your own catalog

Bring your product model and pricing rules and we will walk this workflow with your data.