UC-145
RunRate Value-Based Pricing Engine
Uses AI to recommend optimal pricing by analysing customer behaviour, market conditions, historical transactions, cost drivers, and price elasticity while enforcing pricing policies and margin thresholds.
TBDBuild Duration
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The Challenge
Pricing decisions are often decentralized and based on experience rather than data, resulting in inconsistent pricing, delayed response to changing costs, margin leakage, and missed revenue opportunities.
How It Works
- Ingests quote history with win-loss, realised run prices, tungsten feed and competitor notes.
- Models willingness-to-pay by customer and basin; sets target and floor per quote.
- Adds the materials clause, flags win likelihood live and drafts price-change notes.
What It Removes
- Quotes drifting below what work is worth
- Material spikes reaching prices a quarter late
- No cross-district view of price discipline
Input Data RequirementsQuote history and win-loss records, realised prices of past runs, live tungsten price feed, competitor price notes
Output FormatTarget and floor price per quote, materials-clause wording, win-likelihood indicator, drafted price-change notes
“Material cost jumps flow into prices in days, and every price conversation ships with its evidence.”