Automated Churn Risk Prediction in Construction

Construction-specific AI identifies churn signals by analyzing project-level data - margin realization, schedule variance, RFI response time, change order frequency, and safety incidents - that precede client departure by 60-90 days. Generic churn models miss these drivers because they rely on engagement metrics and email behavior, not field performance. The AI ingests live data from Procore, Sage 300, and Viewpoint Vista, weighting signals differently based on project size, contract type (fixed-price vs. T&M), and whether work is prevailing wage. A 15% margin compression on a $10M project carries different churn risk than the same compression on a $500K job - the model understands this context.

Yes. Revenue Institute maintains SOC 2 Type II compliance and encrypts all data in transit and at rest. We operate a zero-retention LLM policy - construction project data is never used to train large language models or shared with third parties. All client relationship data remains in your environment or in isolated, role-based access controls. We comply with OSHA 29 CFR 1926 requirements around safety data handling and AIA billing format confidentiality. Your Procore credentials, Sage 300 financials, and project intelligence stay proprietary. Audit trails log every data access point so you maintain full visibility into who accessed what and when.

Deployment typically takes 10-14 weeks: weeks 1-2 cover system integration (connecting Procore, Sage 300, Viewpoint Vista APIs), weeks 3-6 involve historical data ingestion and model training on your past 24 months of project performance, weeks 7-10 focus on validation and tuning (ensuring the model accurately flags your actual churn cases), and weeks 11-14 cover go-live, team training, and alert calibration. Most construction clients see measurable results - first churn interventions and retained revenue - within 60 days of production launch. The model improves continuously as new project data flows in.

The AI identifies churn signals by analyzing project-level data such as margin realization, schedule variance, RFI response time, change order frequency, and safety incidents - factors that precede client departure by 60-90 days. Generic churn models miss these drivers because they rely on engagement metrics and email behavior, not field performance.

The AI ingests live data from Procore, Sage 300, and Viewpoint Vista, and weights the churn signals differently based on project size, contract type (fixed-price vs. T&M), and whether the work is prevailing wage. For example, a 15% margin compression on a $10M project carries different churn risk than the same compression on a $500K job, and the model understands this context.

Revenue Institute maintains SOC 2 Type II compliance and encrypts all data in transit and at rest. They operate a zero-retention LLM policy, meaning construction project data is never used to train large language models or shared with third parties. All client relationship data remains in the client's environment or in isolated, role-based access controls. They also comply with OSHA 29 CFR 1926 requirements around safety data handling and AIA billing format confidentiality.

Deployment typically takes 10-14 weeks: weeks 1-2 cover system integration (connecting Procore, Sage 300, Viewpoint Vista APIs), weeks 3-6 involve historical data ingestion and model training on the client's past 24 months of project performance, weeks 7-10 focus on validation and tuning, and weeks 11-14 cover go-live, team training, and alert calibration. Most construction clients see measurable results - first churn interventions and retained revenue - within 60 days of production launch, and the model improves continuously as new project data flows in.