Watch Rafael take four files from a field-service company's existing systems — service agreements, visit history, AR data, and service notes — and turn them into a ranked list of which agreements are at risk, the specific signals each one is sending, and what's still saveable.
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AI changes when you can act on a churn signal — early, while the customer is still under contract and you still have techs in the building. The non-renewal isn't the problem. Acting too late is the problem.
| Customer | ACV | Risk Score | Primary Risk | Evidence | Save Play |
|---|---|---|---|---|---|
| CommonSpirit Health | $187,000 | 82 | Service-quality | PM compliance 50%. 3 callbacks in 90 days. Note 4/12: "Customer asked about other vendors." | Make-good visit + branch manager call |
| Intermountain Health | $142,000 | 67 | Billing-relationship | 3 disputed invoices in 6 months. AR aging trend degrading. Open balance $24K at 75 days. | AR conversation before renewal |
| University of Denver | $96,000 | 54 | Contact-change | New facilities director started March. No intro meeting logged. QBR postponed twice. | Introduction meeting within 30 days |
Example data shown. Actual output will be based on your service agreement data, visit history, AR records, and service notes.
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