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Hive CPQ hivecpq.com

Can AI replace Hive CPQ?

The mechanical core of CPQ is not mysterious: a product tree, option groups, compatibility rules, a pricing formula, a PDF at the end. An agent can produce a working configurator for one product family in a weekend, and if you sell a few hundred SKUs with predictable option logic, that build may genuinely be enough. What you are not one-shotting is the part that makes CPQ a purchase: ERP and CRM sync, dealer accounts with their own price lists and discounts, 3D or 2D visual previews, multi-language catalogs, and someone maintaining the rule set when engineering changes a hinge. Also worth being honest that CPQ failure is expensive: a bad rule ships a quote that cannot be manufactured at the price you promised. So: buildable, and a bad idea to trust for anything you actually invoice on until the rules have been beaten on for months.

Verdict: Half-bot · AI gets you partway; the hard part stays hardBuild time: a weekend
Half-bot

01What it costs

Price variesCustom quote, quoted, B2B manufacturing contract

Checked Aug 18, 2026 · source: hivecpq.com.

02Could AI build it for you?

The core job: A local web app where you define option groups, compatibility rules and pricing formulas in a config file, then walk through a guided configurator that validates choices, prices the result and exports a branded quote PDF.

What a working version needs:

  • Your own product rules written down: option groups, incompatibilities, required combinations
  • A pricing model you can express as formulas, base price plus option deltas plus margin
  • Node 20 and a place to run it, local or a small VPS
  • Manual re-entry of accepted quotes into whatever ERP or accounting system you actually use

A configurator is a weekend; a maintained rule set for a real catalog is a job. Fine build if you are the whole company, wrong build if quotes leave your hands.

03What you'd give up

  • ERP and CRM integrations, so accepted quotes become copy-paste work
  • A dealer or reseller portal with per-account price lists, discount tiers and order history
  • Visual product previews, 3D or otherwise, which is often the thing that closes the sale
  • Anyone but you maintaining the rule engine when the catalog changes
  • Version control on quotes and catalogs, audit trails, and the boring guarantees a buyer expects when a quote is contractual

Because the software is the cheap part. Manufacturers pay for a vendor who will sit with their engineers, turn a messy catalog and a folder of Excel price lists into a rule set that does not produce impossible configurations, then keep it working as products change and push results into ERP. A solo build covers your own product line if you are the engineer, the salesperson and the person who maintains the rules. A company with fifty dealers and a configurable machine cannot staff that with one person and a JSON file.

05The build prompt

Paste this into an AI coding tool (such as Claude, ChatGPT, Lovable or Replit) to build your own version.

prompt.txt
Build a local configure-price-quote tool for a single manufacturer with a configurable product line.

Stack, no substitutions: Next.js App Router with TypeScript, Tailwind, SQLite via better-sqlite3, PDF generation with @react-pdf/renderer. Runs with npm run dev. No auth provider, no cloud services, no telemetry.

Catalog definition lives in /catalog/*.yaml, loaded at startup and validated with zod. A catalog file defines: product families, option groups (each with a type of single-select, multi-select or numeric), options with id, label, price delta and optional lead time, and rules. Support three rule kinds: requires, excludes, and a constraint expression evaluated against the current selection. Fail loudly on invalid catalog files, do not silently skip.

Pricing: each option contributes a delta; support a per-family formula string, evaluated with a small safe expression evaluator over selected values and quantities, plus configurable margin and discount percentages. Show a live price breakdown line by line, never just a total.

Configurator UI: pick a family, then step through option groups. Invalid options are disabled with the rule that blocked them shown in plain text. Selections persist in the URL so a configuration is shareable as a link.

Quotes: save a configuration as a quote with customer name, quote number, valid-until date, notes and line items. Quotes are immutable once marked sent; edits create a new revision that references the previous one. List view with filter by status.

PDF: one branded quote template reading company name, address and logo path from .env. Include the option breakdown, totals, lead time and validity date.

Seed with a fake product family of about twenty options and at least four interacting rules, so the rule engine is exercised on first run.

Include a rules test suite with vitest: for each catalog file, assert that seeded valid configurations price correctly and that known-invalid combinations are rejected.

Out of scope, do not build: ERP or CRM integration, 3D or image visualization, dealer accounts and per-customer price lists, multi-currency, tax calculation, email sending, payments, multi-tenancy.

Write a README covering catalog file format, rule syntax, and a blunt warning that pricing rules must be tested before any quote leaves the building.
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App prices, verdicts, alternatives and build prompts are adapted from Can I Vibecode It? (MIT License, © 2026 Rob Hallam). Each price shows the date it was checked and its source. Prices change; confirm on the vendor's site before you decide.