Winning More Deals with AI-Powered Pricing
How a Global Pump Manufacturer Boosted Sales Efficiency with AI‑Powered Quoting and Pricing Intelligence
The Challenge
As part of a modernization initiative, the company set out to bring structure and consistency to its configure-price-quote process through SAP CPQ. Two critical gaps remained, both of which limited sales effectiveness as volumes and complexity grew.
Manual quote preparation
Sales reps had to manually extract product IDs, descriptions, and quantities from incoming customer requests and enter them into SAP CPQ line by line. At high volumes, this became a significant drag on both speed and accuracy.
Pricing decisions made without data
When configuring products and setting prices, sales reps had no systematic way to leverage the company's historical sales data. Pricing was largely based on individual judgement, leading to inconsistency across the team.
Missed margin opportunities
Without visibility into what had historically won deals at what price points, sales reps were poorly positioned to optimize margins or anchor negotiations with confidence.
Longer negotiation cycles
A lack of data-backed pricing confidence prolonged deal cycles, as sales team struggled to hold their positions without supporting evidence.
The Solution
Graip.AI was deployed as a two-component solution integrated directly with SAP CPQ, addressing both the data entry bottleneck and the pricing intelligence gap in a single, unified workflow.
Intelligent document ingestion
Users upload or forward customer request files directly into the solution. The platform automatically extracts product IDs, descriptions, quantities, and business partner information, eliminating manual re-keying entirely.
Automated product recognition and validation
The solution recognizes and validates every product in real time against the company's master data, ensuring only accurate, matched items proceed to quote population.
ML-driven pricing recommendations
When AI mode is selected, the pricing engine analyses 12+ months of historical sales order data and surfaces a comparison table of previously configured and sold products, complete with pricing and relevant parameters. Recommendations are only shown when the AI model's confidence exceeds a defined threshold. For new products without sufficient history, the system notifies the user transparently rather than generating unreliable estimates.
Seamless SAP CPQ quote population
Validated products and quantities are pushed directly into SAP CPQ via API, supporting both new quote creation and population of existing quotes.
Side-by-side configuration comparison
Built directly within SAP CPQ, the configuration comparison feature allows sales reps to evaluate options side by side, including AI-recommended configurations drawn from historical data. Once the optimal configuration is identified, the representative selects it and finalizes the quote.
Human-in-the-loop design
Throughout the entire process, the sales rep retains full authority over every decision. The solution informs, supports, and does not override.
The Implementation
We delivered the solution through Graip.AI's structured three-phase methodology:
ALIGN (Discover & Blueprint)
Graip.AI worked with the client to identify the two highest-impact gaps in the quoting process, manual document intake and inconsistent pricing, and defined the scope and integration requirements for both components.
PROVE (Build, Validate & Launch)
The intelligent document processing component was built and validated against live customer request data. In parallel, the ML pricing model was trained on 12+ months of historical sales orders, with confidence thresholds agreed collaboratively with the client to ensure recommendations met a quality bar the sales team could trust.
SCALE (Measure & Expand)
Following user acceptance testing, both components went live alongside the SAP CPQ implementation. The sales team adopted both modes, standard master data pricing and AI-assisted pricing, with full flexibility to switch based on the quoting context.
The Results
The solution delivered measurable impact across quoting efficiency, pricing consistency, and revenue performance.
By removing manual entry from the front end of SAP CPQ and equipping sales representatives with historically validated pricing intelligence, the company gained a compounding advantage, faster quotes, more confident pricing, and a higher rate of first-proposal wins.
$6M
$6M projected additional annual revenue from a 5% improvement in win rate
The Strategic Advantages
Pricing confidence that closes deals faster
Sales representatives armed with historically validated pricing are better positioned to hold their positions in negotiations and close deals on the first proposal. This reduces the back-and‑forth that extends sales cycles.
Margin protection at scale
Data-driven pricing reduces unnecessary discounting across the team, not just for experienced representatives but consistently, regardless of tenure or individual judgement.
A foundation that improves over time
The ML model is trained on the company's own sales history, meaning its recommendations become more accurate and relevant as more data accumulates, creating a compounding advantage over time.
Conclusion
By deploying Graip.AI across both the document intake and pricing layers of SAP CPQ, the company eliminated two distinct but connected sources of inefficiency in a single implementation. Quote preparation time dropped by 73%, pricing decisions became data-backed rather than intuition-driven, and the sales team gained the tools to win more deals while protecting margins.
With a projected 5% improvement in win rate translating to approximately $6 million in additional annualized revenue, the business case for intelligent quoting automation is clear.
Ready to bring AI-powered intelligence to your quoting and pricing process? Contact us today for a workflow audit.
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