CPQ ARCHITECTURE CASE STUDY

Quote complex supplier catalogs without spreadsheet pricing or mystery math.

One guided sales flow can handle different supplier price books, size rules, compatibility restrictions, freight, and margin policy. Underneath, pricing stays deterministic, versioned, testable, and explainable—no LLM decides the price.

Fewer manual lookupsInvalid orders blocked earlyMargin guardrailsEvery price explainable
WHY THIS MATTERS TO A BUSINESS

The architecture is useful only if it makes quoting safer and easier to operate.

For teams still moving between supplier PDFs, spreadsheets, and tribal knowledge, this pattern creates one controlled path from configuration to customer quote.

Quote faster

One guided workflow replaces repeated manual price-book lookups and spreadsheet formulas.

Prevent order errors

Invalid size, fabric, motor, and option combinations are stopped before purchasing or fulfillment.

Protect margin

Quote discounts recalculate realized margin and can require approval before the quote is finalized.

Change suppliers safely

Versioned rules and price books preserve how an old quote was calculated even after pricing changes.

This is a reference implementation, not a fabricated client case study. I am showing the operating pattern and the executable proof rather than inventing savings numbers.

THE CASE STUDY

The hard part is not the quote screen. It is owning the rules behind it.

A CPQ system becomes expensive to maintain when supplier behavior leaks into forms, components, and one-off conditionals. The architecture has to make change safe before it makes quoting fast.

01

Problem

Suppliers price the same type of product differently. Width and height rounding, product eligibility, fabrics, motors, options, grids, surcharges, and freight all vary.

02

Constraint

A salesperson needs one simple workflow, but the result still has to be deterministic, testable, explainable, and reproducible months after supplier prices change.

03

Tradeoff

I kept supplier behavior in versioned rule data and kept the core engine supplier-agnostic. The demo uses a modular monolith instead of premature microservices.

04

Result

The same quote flow can price multiple suppliers and products, block invalid combinations, preserve provenance, protect margin, and produce a customer-ready quote.

ENGINEERING JUDGMENT

Three decisions matter more than the framework.

PRICE AUTHORITY

No LLM in the pricing path

AI can help ingest catalogs or assist admin work. It should not decide the authoritative price. Pricing stays deterministic and regression-testable.

CHANGE MANAGEMENT

Version the rules, not just the code

Every successful quote carries the supplier rule version and price-table version so old quotes can be reproduced after future price changes.

SYSTEM SHAPE

Modular first, distributed later

A clean modular monolith keeps the first production release easier to ship and debug. Split services only when scale or ownership boundaries justify it.

WHAT I DELIBERATELY AVOIDED
Giant supplier if/else chainsPricing logic in React componentsOpaque AI-generated pricesMicroservices before the problem needs them
PROOF, NOT PROMISES

The architecture is backed by executable evidence.

The repository is public, the pricing engine is covered by regression tests, and GitHub Actions runs the test suite and production build before changes are merged.

ConfigurationValidationCompatibilityRoundingPrice gridSurchargesFreightMarginQuote
NOW TRY IT

The case study explains the judgment. The demo proves the behavior.

The demo opens with a pre-priced Alpha configuration, so the calculation trail and price breakdown are visible immediately. Then build a multi-room quote, switch suppliers, trigger a rule conflict, and test the margin guard.

NEED THIS FOR YOUR SUPPLIER CATALOG?

Let's turn the rulebook into a system your sales team can actually use.

I can help scope the supplier model, pricing rules, quote workflow, approval controls, integrations, and production architecture.