Wholesale distributors and B2B manufacturers face a quoting problem that most CPQ software was not designed to solve: a customer sends an unstructured email or PDF listing parts in their own terminology, using their own part-number formats, and a sales rep must manually identify each product, check catalog availability, retrieve the right customer-specific pricing tier, and return a finished offer — quickly enough to win the order before a competitor quotes first. For high-volume distributors, this manual loop caps daily output at whatever one person can resolve in a working day.

Most CPQ tools were built for the outbound sales model — a rep selecting from a structured product menu, with the platform enforcing pricing rules and generating a polished document. The inbound inquiry model, where the customer drives the conversation with an unstructured request, is where traditional platforms struggle and where a new category of AI-native tools has emerged to fill the gap.

The fundamental split: inbound RFQ versus outbound CPQ

Traditional quoting and CPQ platforms — PandaDoc CPQ, DealHub, Proposify, QuoteWerks — assume a structured catalog and a rep who initiates the quote. They are excellent at enforcing margin rules, routing discounts for approval and integrating with CRM systems. What they require is a human to translate the customer’s request into the platform’s language first: matching the customer’s informal description to a SKU, looking up the right pricing tier, handling a product listed under a different name. That translation step is where distributor productivity is lost.

AI-native tools like Go Autonomous and OferIQ automate that step. The AI reads the customer’s email or attached PDF, resolves each item against the distributor’s catalog — handling synonyms, alternate part numbers and free-text descriptions — applies customer-specific pricing rules, and outputs a draft offer for human review before sending. The rep reviews and approves rather than builds from scratch.

For a technical wholesaler with a large catalog, the difference between “rep builds the quote manually” and “AI drafts it, rep reviews” can mean the difference between six quotes a day and many times that volume. That throughput shift is what makes the AI-native approach genuinely different from traditional CPQ, not just faster CPQ.

What Salesforce CPQ’s end-of-sale means for distributors in 2026

Salesforce entered its native CPQ product into End-of-Sale status on March 19, 2025. Existing customers can continue using it under a maintenance-only roadmap, but distributors evaluating new quoting tooling should not build on Salesforce CPQ going forward. The market impact has been real: DealHub and Conga CPQ (which acquired PROS’s B2B business in February 2026) have both positioned heavily to capture migrating customers. Neither solves the inbound RFQ parsing problem, but both are credible replacements for the outbound CPQ workflow that Salesforce CPQ covered.

How to match the tool to your quoting bottleneck

The right tool depends almost entirely on where the friction sits:

If the bottleneck is parsing freeform inbound customer emails: Go Autonomous (for large operations with enterprise ERP) or OferIQ (for 10-to-150-person distributors) are the relevant tools. Neither requires manual line-item entry from the rep; the AI does the catalog matching.

If the bottleneck is quote workflow, approvals and CRM sync: PandaDoc CPQ, DealHub or Proposify address this. All three offer free trials or transparent pricing, which makes evaluation feasible without a full sales cycle.

If you are an IT or office supply VAR quoting from multiple distributor price files: QuoteWerks (30 years in the IT channel, $50–$102 per concurrent user per month, verified June 2026) or VARStreet InstaQuote (50-plus distributor integrations live in one interface) are the established choices for that vertical.

If you have outgrown your quoting tool and need CPQ, contracts and billing as one system: DealHub is the most complete option, particularly if you are migrating from Salesforce CPQ.

The vendor-stated performance figures from AI-native platforms — Go Autonomous’s 57-second average quote time, OferIQ’s reported volume lift for sales teams — should be validated in a live demo against your own catalog before any commitment. Catalog complexity varies enormously by distributor, and real-world performance depends on how well your customer terminology aligns with your catalog structure, how many product variants need disambiguation, and whether your pricing rules are consistently documented.

What to verify before signing anything

Pricing transparency is the sharpest weakness in this category. QuoteWerks and Proposify publish their rates; every other platform on this list requires a sales conversation to get real numbers. Before piloting anything, get the all-in annual cost — seat minimums, implementation fees, per-quote or per-document overages — not just the monthly per-seat figure. AI-native platforms like Go Autonomous often price on transaction volume or project scope rather than seats, which means a cost model that looks flat can scale faster than expected as usage grows.

Trial quality also varies. A 14-day free trial lets you test with real deals; a “30-minute demo on your data” gives a more honest read on fit than a generic sandbox when the question is whether the AI can handle your specific inventory format and customer terminology. For catalog-heavy distributors evaluating an AI parsing tool, the demo against your actual catalog is more diagnostic than any benchmark figure.