Use Case — Operational Intelligence
Most analytics investments produce dashboards. Dashboards show what happened. Operational intelligence shows what is happening, predicts what will happen next, and recommends — or takes — action to change the outcome. That shift, from retrospective reporting to predictive operational loops, is where data investments start delivering returns that show up in the P&L.
We have built operational intelligence systems across utility, retail, and consumer platform environments — from pump failure prediction at a water utility serving 2 million customers, to real-time billing anomaly detection protecting revenue at scale, to supply chain analytics at a major Texas retailer delivering promotional forecasting, out-of-stock inference, and trending item analysis that directly informed buying and replenishment decisions.
The pattern is consistent: take operational data that previously fed a lagging dashboard, apply ML models that predict failure, anomaly, or demand, and close the loop with an automated response or a prioritized recommendation to the right operator at the right time.
The Shift
What We Build
ML models trained on equipment sensor data, maintenance logs, and operational telemetry that predict failure probability before it happens — giving maintenance teams a prioritized work queue based on actual risk, not scheduled intervals. Built for utilities, manufacturing, and infrastructure-heavy operations.
Statistical and ML-based anomaly detection across financial, operational, and customer data — flagging billing errors, fraud signals, data quality issues, and operational deviations in real time. Deployed enterprise-wide with tools like Monte Carlo for data observability and custom ML models for domain-specific patterns.
Promotional demand forecasting, out-of-stock inference, and trending item analysis that gives buyers and replenishment teams data-driven inputs rather than instinct-based decisions. Built on historical transaction data, external signals, and ML models that improve with every new period's data.
Moving from static dashboards to operational views that surface prioritized recommendations alongside the data — so the person looking at the screen knows not just what the KPI is, but what action to take in response. Closing the gap between insight and action.
Ingestion pipelines for sensor, wearable, and edge device data — connecting field telemetry to analytical systems in real time. Built at HeartSync for Garmin biometric data and designed for utility, industrial, and infrastructure environments where IoT data drives automation, predictive maintenance, and cost-effective service delivery.
Real-time analysis of customer activity patterns — identifying engagement drops, billing anomalies, and churn signals as they emerge rather than in retrospect. Closing the loop with automated outreach or operator alerts that turn a signal into a retained customer.
Proven at Scale
At a major Texas public utility serving millions of customers, we directed the implementation of predictive analytics for pump failure prediction and billing anomaly detection. Pump failure prediction uses sensor telemetry and operational data to score equipment risk before failure occurs, enabling maintenance crews to prioritize interventions based on actual predicted failure probability rather than fixed schedules. Billing anomaly detection flags revenue exceptions in real time, protecting the utility's financial position at population scale.
At H-E-B — a $38 billion Texas retailer serving 10 million customers — we built advanced supply chain analytics that delivered promotional demand forecasting, out-of-stock inference, and trending item analysis. These weren't dashboards reporting last week's stockout rate. They were predictive models informing buying and replenishment decisions before the stockout happened — directly improving in-stock rates and reducing the excess inventory that comes from over-buying as a hedge against uncertainty.
At Slickdeals, we deployed Monte Carlo enterprise-wide for data quality monitoring, anomaly detection, and freshness alerting — ensuring that the dashboards business users relied on for daily decisions reflected accurate, current data rather than silently stale or corrupt figures.
Let's Talk
Tell us about the operational data you have, the failures or anomalies that cost you most, and where better prediction would change your decisions.