- Features > Conversational CPQ
Conversational CPQ
Buyers describe what they need in plain language. Mercura resolves the request against your catalog, validates it against your rules, and returns a real, priced, valid quote — not an AI hallucination.
Natural language
Buyers describe needs — no configurator training required
Real quotes
Validated against rules and pricing — never hallucinated
Multi-turn
Follow-up questions and clarifications until the spec is complete
The Challenge
Configurator UIs Are Friction — Buyers Want to Describe What They Need
A traditional configurator demands that the buyer learn a sequence: pick a product family, select a model, choose an option set, set parameters, view the result. For an experienced buyer this is fine; for an occasional buyer or a procurement specialist working across many categories, the click path is friction that delays the quote and increases abandonment.
Buyers increasingly expect to interact with B2B catalogs the way they interact with consumer AI assistants — by describing the outcome they need in natural language. "I need a 60W warm-white downlight rated IP65 for a parking garage," not a four-step configurator flow.
The naive approach — wiring a generic chatbot to a product catalog — fails badly. Chatbots hallucinate products that do not exist, miss compatibility rules, return wrong prices, and create brand-damaging interactions. The conversational layer must be tightly bound to the rules engine and pricing engine for the experience to be commercially safe.
Manufacturers who solve conversational configuration well unlock a category of buyers who would not have completed a traditional configurator — and dramatically lower the friction for everyone else.
How It Works
How Conversational CPQ Works in Mercura
Mercura's conversational CPQ layer combines a large language model interface with strict grounding in the rules and pricing engines. Buyer inputs in natural language — "I need a 60W warm-white downlight rated IP65" — are parsed into structured configuration requests. The configuration is then resolved against the actual catalog and validated by the rules engine: incompatible combinations are rejected, missing required attributes prompt follow-up questions, and only configurations the catalog can actually deliver are presented. Pricing is applied through the standard pricing engine — customer-specific rates, discount tiers, and approval rules all apply. The buyer sees a normal, valid, priced quote at the end of the conversation. Multi-turn dialogue handles clarifications, alternatives, and substitutions without ever returning a hallucinated product or invalid configuration.
What's Included
Key Capabilities
- Natural language input — buyers describe needs in their own words
- Strict grounding — every response validated against the real catalog and rules
- Multi-turn conversation — clarifying questions when the spec is incomplete
- Hallucination prevention — no product or price returned that the catalog does not support
- Customer-specific pricing automatically applied to conversational quotes
- Substitution suggestions when an exact match is unavailable
- Embeddable conversational widget — drops into any sales portal or storefront
- Multilingual — buyers interact in their preferred language
The Difference
Before and After Conversational CPQ
- Buyers must learn a configurator flow before they can request a quote
- Occasional buyers and procurement specialists abandon configurators
- Generic AI chatbots hallucinate products and invalid configurations
- Conversational experiments fail compliance review — brand risk too high
- Conversational traffic lost to easier-to-use catalogs and competitors
- Buyers describe needs in plain language — no configurator training required
- Every conversational response grounded in real rules and pricing
- Multi-turn dialogue completes the spec without forcing a UI flow
- Brand-safe by design — no hallucinations reach the buyer
- Conversational channel converts buyers who would not have configured manually
Real-World Application
Example Use Case: Lighting Manufacturer Capturing Specifier Traffic
A lighting manufacturer sold complex commercial fixtures through a configurator on their website. Analytics showed that 41% of specifiers — architects, lighting designers, electrical contractors — abandoned the configurator within the first three steps. Interviews revealed that specifiers wanted to describe the fixture they were specifying — "60W warm-white downlight, IP65, 0–10V dimming, 80mm cutout" — rather than navigate a multi-step form. After deploying Mercura's conversational CPQ layer, specifiers could state their needs in natural language; Mercura's rules engine resolved the request to a real, available fixture and presented a priced quote within seconds. Configurator abandonment dropped to 12%, and the average quote turnaround for specifier traffic dropped from 36 hours to 4 minutes.
Quote turnaround dropped from 3 days to under 4 hours.
Business Impact
Why Conversational CPQ Matters
Conversational interfaces are how a generation of buyers now expects to interact with complex products. A configurator that demands a UI flow loses the buyers who would have described their need in a sentence; a CPQ that grounds conversational input in real rules and pricing wins them. The difference between conversational CPQ and a chatbot is not the AI — it is the binding between AI and the rules engine. That binding is what makes conversational CPQ commercially safe, brand-consistent, and trustworthy enough to be the front door to a manufacturer's catalog.
Open Your Catalog to Conversational Buyers
Book a demo to see how Mercura's conversational CPQ turns natural language requests into real, valid, priced quotes.
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