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Why B2B Quoting Errors Are Data Problems and How a CPQ Engine Addresses Them

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The article argues that many B2B quoting issues stem from data problems, such as invalid option combinations, unapproved discounts, and price changes between quote and order, rather than sales issues. It proposes a configure-price-quote (CPQ) engine that turns product rules, pricing, and approvals into versioned data enforced by the system. The author describes three separate engines for configuration, pricing, and approvals, each changing at a different rate and kept separate in code and data. The guide presents a data model in PostgreSQL using numeric money values and immutable quote versions, along with a Laravel-based pipeline for rule validation. The author notes the piece was written with AI assistance and that all queries and code samples were manually verified.

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