AI CPQ Solver
Customer entered bay width and duty cycle. CPQ returned forty-two valid configs. The rep still escalated to engineering. Mercura AI solver ranks rule-valid options against stated goals with reasoning your team can override.
The challenge
Customer entered bay width and duty cycle. CPQ returned forty-two valid configs. The rep still escalated to engineering.
A builder of overhead bridge crane systems for manufacturing and logistics bays configures span, capacity class, hook height, runway speed, and control package in Mercura CPQ. Rules block incompatible trolley and rail combinations. A buyer enters bay width, required lift capacity, and expected duty cycle. CPQ returns dozens of rule-valid line items that all price correctly.
Inside sales still pings application engineering because none of the forty-two options clearly wins on cost, lead time, and floor clearance together. Reps pick the third ranked config by habit. Customers receive quotes that work on paper but are heavier or slower than the application needs. Expert judgment does not scale when every inquiry leaves a wide valid set.
AI CPQ implementation bootstraps draft rules from ERP and specification uploads. Conversational CPQ parses buyer language during quoting. Agentic CPQ governs procurement agent endpoints. Rules-based configuration rejects invalid combinations but does not rank valid ones. The AI CPQ solver is different: Mercura searches the rule-valid configuration space, scores candidates against cost, performance, lead time, and regulatory objectives you define, and returns ranked recommendations with plain-language reasoning reps review before quote send.
Inquiry to config to price to approval to order should not queue behind engineering every time constraints leave more than one correct answer.
How it works
How the Mercura AI CPQ solver works
Buyers or reps enter application requirements: span, capacity, duty class, clearance limits, budget ceiling, or regulatory tags. Mercura runs the rules engine first so only valid configurations enter the solver search. The AI evaluates candidates against weighted objectives your product team configures, cost, performance, lead time, and compliance priorities. Ranked results include comparison rationale and confidence indicators. When trade-offs exist, the solver surfaces Pareto-optimal alternatives instead of hiding the tension. Sales engineers accept, adjust, or override recommendations before pricing and quote release. Historical approved configurations inform scoring over time. Mercura does not replace rules authoring, approval policy, or SME sign-off on novel applications.
What's included
What the AI CPQ solver covers
- Requirement inputs mapped to rule-valid configuration search
- Multi-objective scoring across cost, performance, and lead time
- Ranked recommendations with plain-language comparison rationale
- Pareto-optimal alternatives when goals conflict
- Confidence indicators for how well each option meets requirements
- Rep review and override workflow before quote send
- Learning signal from approved configurations and outcomes
- API access for solver calls in custom sales tools
The difference
Complex configuration before and after AI ranking
- Dozens of rule-valid configs with no clear best fit
- Quality depends on which engineer or rep handles the inquiry
- Complex quotes queue behind application engineering
- Self-service stops when more than one valid answer exists
- Customers receive workable but suboptimal specifications
- Ranked shortlist from customer requirements in one session
- Same scoring logic applied to every inquiry and channel
- Engineering reviews exceptions, not every multi-option quote
- Self-service extends to applications with many valid paths
- Quotes reflect best-fit trade-offs reps can explain to buyers
Real-world application
Example workflow: crane config ranked by span, duty, and lead time
An overhead crane OEM saw reps escalate whenever bay width and duty cycle left more than thirty valid Mercura configurations. After enabling the AI solver, a buyer entered span, capacity class, and expected picks per hour. Mercura returned a ranked shortlist with reasoning on rail weight, motor package, and quoted lead time. The rep accepted the top recommendation, adjusted control package for a regional safety tag, and sent the quote without an engineering callback. Application engineers now handle novel bay layouts only.
Business impact
Why the AI solver is ranking intelligence, not a replacement for rules
The AI CPQ solver adds engineering judgment at quote time when rules alone leave multiple correct answers. It complements AI CPQ implementation, sales guidance flows, rules-based validation, and self-service configurators. Mercura does not bypass the constraint engine or publish recommendations without rep review. Someone must define objectives, validate scoring after catalog changes, and own approval on edge cases. If the pain is "CPQ says valid but nobody knows which valid option to quote", the solver aligns inquiry, configuration, price, approval, and order with ranked configs your team can defend to the buyer.
Enter span and duty cycle and review a ranked crane shortlist before quote send
Book a demo and walk requirement input through rule-valid search, ranked recommendations, and rep override until engineering escalation drops on multi-option inquiries.
Let’s build together.
We empower manufacturers to master product modeling, streamline quoting process, reduce errors, and ultimately deliver the tailored solutions that customers demand.