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. The system runs inside your own environment under your existing permissions, and all data is encrypted in transit and at rest. We operate a zero-retention AI policy - construction project data is never used to train AI models or shared with third parties. All client relationship data remains in your environment or in isolated, role-based access controls. The system reads safety and billing records from your existing platforms without re-housing them, so the OSHA recordkeeping and AIA billing confidentiality practices you already run stay intact. 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.

Plan for a working system inside the first 100 days: 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. A rollout like this is scoped to show measurable results - first churn interventions and retained revenue - within 60 days of production launch. The model improves continuously as new project data flows in.

Five streams, all of which you already collect: job cost actuals versus budget from Sage 300 or Viewpoint Vista, RFI and submittal logs from Procore, schedule variance and milestone tracking from Autodesk Construction Cloud, change order records, and safety incident data. None of it requires new data entry - the model reads what your project teams already produce, then connects field performance to relationship health.

Every signal gets weighted against the job it came from. Margin compression on a $10M fixed-price project is a different animal than the same percentage on a $500K T&M job, and prevailing-wage public work gets its own baseline - Davis-Bacon pressure compresses margins in ways that have nothing to do with the client relationship. Your historical project data sets those weightings during training, so risk scores reflect how your book of work actually behaves.

Firms with a small, stable client base where ownership already knows every relationship personally, or firms whose Procore, Sage 300, and Viewpoint Vista data is too fragmented to reflect actual project performance - the model inherits that gap instead of closing it. At that scale the math rarely clears, and we will say so. This is built for firms running enough concurrent projects and client relationships that early warning signals get lost before a partner or PM ever sees them. Your current marketing and business development team stays either way - the system flags the risk, it does not replace the relationship. If you are not sure which side of the line you are on, the free AI Opportunity Assessment will tell you.