From first connection
to first outcomes.
In 30 days.

Connect™ establishes live connections to your source systems — typically a combination of your primary operational platform (PMS, CRM, claims system, EHR) and one or two supplementary sources (loyalty database, incident log, billing feed).

Field mapping translates your system's native field names to the Vavoris signal schema. Mappings are broadcast to the SignalMapperJob at runtime — you can adjust them without restarting the connector. By end of week one, signals are flowing continuously into the decision loop.

Observe™ applies the deployed rule graph to live signal flow. The scoring engine evaluates each signal against its context — entity history, loyalty tier, claim history, patient risk — and produces a weighted score per event.

This week the team reviews the top-scoring signals daily to validate that the graph is catching real operational events, not false positives. Threshold values are adjusted based on observed signal patterns. By end of week two, you have a calibrated baseline for what a genuine signal looks like in your specific operational context.

Decide™ selects the best next action for each scored signal. Govern™ enforces your governance policy — pilot deployments typically start in Human Approval mode, where every recommendation requires a staff member to confirm before action is taken.

Act™ delivers recommendations through your existing channels — email, Slack, a PMS screen pop, or a case management note. Staff receive recommendations with plain-language explanations of why each action was suggested. Every approved and rejected recommendation is recorded in the audit trail with the operator's reason.

Learn™ measures whether the actions taken produced the intended outcomes. For hospitality, this means guest retention signals. For insurance, claim settlement comparisons. For healthcare, 30-day readmission tracking. Every outcome is linked to the specific decision ID, graph version, and signal that triggered it.

By end of week four, you have a measurable baseline: how many recommendations led to the intended outcome, what the first accuracy score is, and what the calibration starting point looks like. This is the baseline that will compound every month afterward.

At day thirty, the pilot converts to a production deployment. The decision loop that ran in Human Approval mode during the pilot continues — now with the option to introduce Hybrid or Guarded Automation governance for signal types that have demonstrated sufficient accuracy.

The calibration baseline established in week four begins to compound. Every night, GraphCalibrationJob processes the previous day's outcomes and updates the graph. Every month, the shadow evaluation job tests whether a better graph has emerged.