Pulse Analytics

Teams running a business off a BI dashboard usually find out a KPI moved only after it's already moved — dashboards are built to describe history, not to warn about what's coming. By the time a metric crosses a threshold on a static chart, the window to act on it has often already closed.

We built Pulse Analytics around a different assumption: every KPI on the dashboard should carry a forecast, not just a historical trend line. That meant treating the platform's data pipeline and its modeling layer as one system from the start, rather than bolting predictions onto an existing reporting tool after the fact.

Each tracked metric is fed through a forecasting model that projects its expected range over the coming days, and flags a reading as anomalous the moment it drifts outside that range — not after a human notices it on a chart. Under the hood we mix classical time-series forecasting for stable, seasonal metrics with gradient-boosted models for KPIs driven by more complex, multi-factor behavior, and let the platform pick per metric based on backtested accuracy.

The result is a dashboard that surfaces "this is about to become a problem" instead of "this became a problem three days ago." Anomaly alerts fire well before a metric would have crossed a manual threshold, giving teams a meaningful head start on revenue dips, churn spikes, or supply issues before they show up in the monthly report.