Automated Deal Desk Pricing in Manufacturing

AI engines ingest live production data from your ERP and MES platforms, then model the true marginal cost and feasibility of each quote request by checking machine capacity, material availability, lead times, and labor constraints in real time. Instead of sales emailing production planning and waiting for a spreadsheet, the AI recommends a price and delivery window in seconds - factoring in whether the order requires expedite, premium sourcing, or overtime. The recommendation protects margin by pricing orders based on actual plant floor state, not conservative estimates. Over time, the model learns which quotes actually ran profitably and which delivery dates slipped, continuously improving accuracy.

Yes. Data remains encrypted in transit and at rest within your secure environment. For Manufacturing clients subject to ITAR export controls or customer-specific data agreements, we support air-gapped deployments and role-based access controls that enforce your compliance requirements. Deal desk pricing recommendations are generated on-premise or in your private cloud instance, ensuring sensitive pricing and capacity information never leaves your network.

Deployment typically takes 10-14 weeks from contract to go-live. Weeks 1-3 focus on data mapping - connecting your ERP, MES, and SCADA systems, validating data quality, and defining your routing and BOM logic. Weeks 4-8 involve model training on 12-24 months of historical quote and production data, then testing recommendations against actual outcomes. Weeks 9-14 cover CRM integration, sales team training, and phased rollout to your deal desk. Most Manufacturing clients see measurable results - faster quotes, improved margin - within 60 days of go-live as the system begins learning your production patterns.

AI engines ingest live production data from ERP and MES platforms, then model the true marginal cost and feasibility of each quote request by checking machine capacity, material availability, lead times, and labor constraints in real time. This allows the AI to recommend a price and delivery window in seconds, factoring in whether the order requires expedite, premium sourcing, or overtime. The recommendations protect margin by pricing orders based on actual plant floor state, not conservative estimates, and the model continuously improves accuracy by learning from past quotes that ran profitably.

Data remains encrypted in transit and at rest within your secure environment. For Manufacturing clients subject to ITAR export controls or customer-specific data agreements, Revenue Institute supports air-gapped deployments and role-based access controls that enforce your compliance requirements. Deal desk pricing recommendations are generated on-premise or in your private cloud instance, ensuring sensitive pricing and capacity information never leaves your network.

Deployment typically takes 10-14 weeks from contract to go-live. Weeks 1-3 focus on data mapping - connecting your ERP, MES, and SCADA systems, validating data quality, and defining your routing and BOM logic. Weeks 4-8 involve model training on 12-24 months of historical quote and production data, then testing recommendations against actual outcomes. Weeks 9-14 cover CRM integration, sales team training, and phased rollout to your deal desk. Most Manufacturing clients see measurable results - faster quotes, improved margin - within 60 days of go-live as the system begins learning your production patterns.

The key benefits of using AI for deal desk pricing in Manufacturing include: 1) Faster quote turnaround times by automatically factoring in real-time production constraints, 2) Improved profit margins by pricing orders based on actual plant floor state rather than conservative estimates, 3) Continuous model improvement as the AI learns from past quote profitability, and 4) Secure data handling that keeps sensitive pricing and capacity information within the manufacturer's own environment.