Salesforce CPQ, DealHub, PandaDoc, and Conga CPQ all promise to turn a complex product catalog into an accurate quote in minutes instead of a sales rep manually checking pricing rules, discount approval thresholds, and product compatibility across a spreadsheet. The differences show up once your catalog gets genuinely complicated, bundled products, tiered volume pricing, approval chains that change based on discount depth, not in the demo's simple three-item quote.
Most companies adopt CPQ software because quoting has started taking too long or producing errors a sales rep didn't catch, then discover the platform they picked assumes a simpler product catalog or approval structure than they actually run. We start from your real catalog, how many products, how configurable each one is, how your discount-approval chain actually works today, and match the platform to that instead of whichever one your CRM vendor bundles in by default. From there, your existing price book, product rules, and approval logic need to move into the new system accurately, which is detailed, unglamorous work we do directly, and if your pricing or approval structure doesn't map onto any CPQ platform's standard configuration model, we build the rules engine that actually represents how your business prices and approves deals.
Salesforce CPQ, DealHub, PandaDoc, and Conga all produce a clean three-line quote in a demo. The real test is your actual catalog: bundled products with dependency rules, tiered volume pricing, and a discount-approval chain that changes based on how deep the discount goes. We compare platforms against that, not a simplified demo scenario.
Your existing price book, discount tiers, and approval-chain logic usually live across a spreadsheet, your CRM's basic pricing fields, or a sales ops team member's institutional knowledge. We migrate that into the new CPQ platform rule by rule, so a quote generated on day one matches what your team would have approved manually.
Some businesses price in ways that don't map cleanly onto a standard CPQ rules engine, usage-based components, custom bundling logic, approval thresholds that shift by product line. When that's genuinely the case, we build the rules engine around how you actually price and approve deals, instead of forcing your sales team to work around a platform that's close but not quite right.
Your actual product catalog, how configurable it is, how your discount-approval chain actually works, decides this more than a vendor's feature sheet. Walk us through your real quoting process and we'll tell you honestly which platform fits, what a real price-book migration looks like, or whether your pricing logic needs something built custom.
Get a free quote → →That depends on how complex your catalog and approval chain actually are. A simple flat-pricing product with few variants often doesn't need a dedicated CPQ platform at all. Bundled products, tiered discounts, and multi-step approvals are usually where a CRM's native quoting starts to break down, and where CPQ earns its cost.
Yes, if it's done rule by rule rather than as a bulk import, which is how we approach it. We test the migrated pricing and approval logic against real past quotes before go-live, so the numbers a rep gets on day one match what they'd have calculated manually.
The major platforms, Salesforce CPQ especially, are built to sit inside or tightly alongside a CRM reps already use. Whether that integration is seamless or just adequate varies by platform and by how customized your CRM already is, which we check before recommending anything.
Most platforms handle tiered, conditional approval logic reasonably well out of the box. Where it gets harder is when the chain also depends on product line, region, or deal size in combination, which is the kind of layered rule we build custom when the standard configuration doesn't flex that far.
We do. Salesforce, DealHub, PandaDoc, and Conga all maintain their own platforms; mapping your real catalog and approval chain into the system, and building custom rules logic when it doesn't fit natively, is the work we handle.
For a catalog of moderate complexity, usually four to six weeks including price-book migration and rule testing. A genuinely complex catalog with custom approval logic can run eight to twelve weeks.