Automated Deal Desk Pricing in Construction

AI deal desk pricing ingests live project data from Procore, Sage 300, and Primavera P6 to surface real-time pricing recommendations tied to your firm's actual cost structure and historical margin performance. The system learns which cost assumptions hold true across project types and geographies, then recommends price bands for new deals within 90 seconds - eliminating weeks of manual estimation. Sales retains full control to adjust recommendations based on competitive or strategic factors, but now negotiates from actual project economics rather than assumptions. The AI continuously improves as it ingests closed-project data, comparing bid estimates against actuals to refine pricing models monthly.

Yes. All Construction-specific data (prevailing wage requirements, OSHA compliance overhead, AIA billing formats) remains encrypted in transit and at rest within your dedicated environment. We segment data access so sales teams see only pricing recommendations, while finance and operations can audit the cost assumptions and margin calculations underlying each deal. Regular security audits confirm compliance with your firm's data governance policies.

Plan for a working system inside the first 100 days. Weeks 1-3 involve data integration - connecting Procore, Sage 300, Viewpoint Vista, and Primavera P6 to the Revenue Institute platform and validating historical project data. Weeks 4-6 focus on model training using your closed deals and margin actuals. Weeks 7-9 include pilot testing with your sales team on 10-15 representative deals, refining recommendations based on feedback. Weeks 10-14 cover full rollout, staff training, and continuous monitoring. A rollout like this is scoped to show measurable results within 60 days of go-live - bid cycles compress first, with pricing accuracy improving as each new closed project feeds the model.

Four systems feed the pricing model: Procore for labor actuals, equipment costs, and RFI timelines; Sage 300 for subcontractor invoices and material escalations; Viewpoint Vista for crew productivity rates; and Primavera P6 for schedule variance and critical path impacts. Each source covers a cost driver that spreadsheet estimates usually hold as a static assumption.

After each project closes, the system compares the bid's cost assumptions against actual job costs and measures the margin variance. Those bid-to-actual comparisons feed monthly retraining cycles, so recommendations tighten for similar project types and geographies as your closed-project history grows. Sales override rationale feeds the same loop - which is why logging it matters.

Access is segmented by role. Sales sees the price band and the risk flags. Finance and operations can open the full audit trail - the cost assumptions, subcontractor rates, and margin calculations behind each deal - so pricing decisions can be reviewed after the fact. Estimators keep final say on every bid; the system recommends, it does not approve.

Bid-cycle time. The pilot runs on 10-15 representative deals so your sales team can check recommendations against work they already know, and the first measurable result is quotes moving out the door in days instead of weeks. Pricing accuracy improves more gradually - each closed project feeds its bid-to-actual variance back into the model, so the recommendations tighten as your project history grows.

Firms under $10M in revenue, or teams where the volume is still low enough for one person to handle comfortably - at that scale the math rarely clears, and we will say so. This is built for Construction firms of 50-500 people where the work is real enough that the default fix would be another process hire. If you are not sure which side of that line you are on, the free AI Opportunity Assessment will tell you.