- Features > Ai CPQ Solver
AI CPQ Solver
Configure complex products intelligently with AI. Mercura's AI solver takes customer requirements as input and recommends optimal product configurations — resolving constraints, meeting performance targets, and minimising cost automatically.
Seconds
To resolve complex multi-constraint configurations
Optimal
Configuration — not just valid, but best-fit
Zero
Sales engineering required for standard AI-solved configurations
The Challenge
Complex Product Configurations Require Expert Engineering Judgment
For manufacturers of technically complex products — industrial machinery, engineered systems, custom assemblies — the configuration process requires more than selecting from a list of options. It requires understanding how different parameters interact, how to meet performance requirements under multiple constraints, and how to balance cost, lead time, and technical specification.
This knowledge is held by experienced sales engineers and product specialists. When they are available, configuration quality is high. When they are not — because demand exceeds their capacity, because they are serving a geography where they are not present, or because a customer is exploring options outside business hours — configuration quality degrades.
Rules-based configuration prevents invalid configurations. But it does not recommend the optimal one. A customer who specifies a required throughput and an available installation footprint may have dozens of valid configurations that meet the constraints — but only a few that are optimal for their application. Identifying the optimal choice requires judgment that rules alone cannot provide.
AI changes this. The engineering judgment needed to identify the optimal configuration for a set of requirements can be learned from historical data and encoded into an AI model that applies it consistently at any time, for any customer, without human involvement.
How It Works
How the AI CPQ Solver Works in Mercura
Mercura's AI solver takes customer requirement inputs — performance specifications, space constraints, budget limits, regulatory requirements, or application descriptions — and generates an optimal product configuration recommendation. The solver searches the configuration space defined by the product's rules engine, evaluates candidates against the customer's stated objectives, and returns the configuration that best satisfies all requirements simultaneously. Where trade-offs exist — for example, between cost and performance — the solver presents the Pareto-optimal options with clear reasoning for each. The AI learns from historical configurations and customer feedback, continuously improving recommendation quality. Sales engineers can review and modify AI recommendations, with the AI's reasoning transparent at each step.
What's Included
Key Capabilities
- Requirement-to-configuration translation — customer specifies outcomes, AI specifies product
- Multi-constraint optimisation — cost, performance, lead time, and regulatory requirements simultaneously
- Pareto-optimal alternatives when trade-offs exist — with clear comparison rationale
- Confidence scoring — AI indicates how well each recommendation meets stated requirements
- Transparent reasoning — every recommendation explained in plain language
- Sales engineer review and override workflow
- Continuous learning from approved configurations and customer outcomes
- API access for AI solver integration into custom tools and workflows
The Difference
Before and After the AI CPQ Solver
- Optimal configuration identification requires experienced sales engineer involvement
- Configuration quality inconsistent — depends on who handles the inquiry
- Rules prevent invalid configurations but do not identify the best valid one
- Complex inquiries queue behind available engineer capacity
- Customer self-service limited to simple configurations — complex ones need human help
- AI recommends optimal configuration from customer requirements in seconds
- Configuration quality consistent — AI applies the same expertise to every inquiry
- Best-fit configuration identified automatically from thousands of valid options
- Complex inquiries resolved instantly — no queue, no wait time
- Self-service extends to complex configurations — AI handles the engineering judgment
Real-World Application
Example Use Case: Pump Selection for Industrial Applications
A pump manufacturer's sales engineers spent a significant portion of their time helping customers select the correct pump model for their application — a process that required understanding flow rate, pressure, fluid properties, installation constraints, and regulatory requirements simultaneously. The selection criteria were well understood but required judgment to apply correctly to each unique application. After implementing Mercura's AI solver, customers entered their application requirements — fluid type, flow rate, pressure differential, installation space, and budget — and received a ranked set of pump recommendations with full application-specific rationale. Sales engineer involvement dropped by 65% for standard applications. For the most complex applications, the AI solver produced a shortlist of candidates that engineers could evaluate in minutes rather than starting from scratch. Customer time-to-quote fell by 58%.
Quote turnaround dropped from 3 days to under 4 hours.
Business Impact
Why the AI CPQ Solver Matters
The AI CPQ solver elevates CPQ from a tool that enforces constraints to a system that applies engineering intelligence. It makes the expertise of your best sales engineers available at scale — consistently, instantly, and across all channels. For manufacturers whose products require genuine technical judgment to specify correctly, the AI solver is the difference between a CPQ system that prevents errors and one that actively drives optimal outcomes for every customer, every time.
Let AI Handle the Hard Configuration Problems
Book a demo to see how Mercura's AI solver recommends optimal product configurations from customer requirements — automatically.
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