Automated Sales Forecasting in Construction

AI forecasting in construction works by analyzing historical bid accuracy, project cost performance, and schedule variance across your completed work to predict revenue probability and margin outcomes for active opportunities. The system ingests data from Procore, Sage 300, and Viewpoint Vista to understand which project characteristics, client patterns, and scope factors drive your firm's actual profitability - not generic sales metrics. This allows sales teams to rank pipeline opportunities by realistic margin and close probability, repricing or declining low-margin work before it consumes resources, and giving estimators continuous feedback on bid calibration.

Yes. All data remains encrypted in transit and at rest, with access controls aligned to your firm's organizational structure. We handle construction-specific regulatory requirements including AIA billing formats, prevailing wage documentation, and LEED certification data, ensuring compliance with Davis-Bacon and local building code requirements while maintaining strict confidentiality.

Plan for a working system inside the first 100 days. Phase 1 (weeks 1-3) involves data integration with your Procore, Sage 300, and scheduling systems; Phase 2 (weeks 4-8) focuses on model training using your historical project data; Phase 3 (weeks 9-14) includes user training and pilot testing with your sales leadership. A rollout like this is scoped to show measurable forecast accuracy improvements and pipeline insights within 60 days of go-live, with the system's predictive power increasing as it ingests additional completed project outcomes.

Forecasting runs on your own historical project and pipeline data inside your environment - nothing moves to an outside platform, and your bid history never trains models used by other contractors. Access follows your firm's existing role structure, and every forecast is logged with the project data behind it, so estimators can audit the reasoning. Data terms are part of the contract.

The model draws from your project management system (Procore), job cost accounting (Sage 300 or Viewpoint Vista), and scheduling (Primavera P6): bid estimates versus actuals, change order logs, RFI response cycles, labor productivity, and schedule variance. CRM pipeline data rides on top, but the predictive power comes from linking each open opportunity to how similar projects actually performed - that link is what generic forecasting tools never make.