Data Science

Patient records, scheduling, and billing usually live in separate systems that don't talk to each other. We build dashboards that bring them together into KPIs your team can actually track, from bed utilization to appointment no-show rates, and forecasting models for things like patient flow once the reporting layer is solid.

Financial data comes with tight accuracy and audit requirements, and pipelines built for other industries usually cut corners here. We build ETL processes with validation and reconciliation built in, then dashboards on top so the numbers match before they reach a report.

Sales, inventory, and foot traffic data rarely sit in one place until someone builds the pipeline that connects them. We turn that into dashboards for stock levels and store performance, plus demand forecasting once you're ready to plan ahead instead of react.

Sensor data, production logs, and ERP records move at different speeds and in different formats on a factory floor. We build pipelines that bring them into one warehouse, so downtime and yield reporting reflect what's happening on the line, with predictive maintenance as a next step.

Customer behavior data piles up fast in e-commerce, but most of it never gets used past a basic sales dashboard. We build reporting on top of that data plus models for churn risk, product recommendations, and demand forecasting scoped to what you already collect.

Route data, delivery times, and fleet costs are usually tracked well enough to report on, but not well enough to predict. We build the pipelines to consolidate that data and forecasting models for demand and delivery windows that help plan capacity ahead of time.

Portfolio performance, occupancy, and pricing data often live in spreadsheets that get rebuilt every reporting cycle. We build dashboards that track these automatically, so a portfolio review doesn't start with a week of manual reconciliation.

Enrollment, attendance, and outcomes data are usually spread across systems that were never meant to talk to each other. We build reporting that brings them together, so administrators can track trends without exporting three spreadsheets to compare them.

Usage data is the most valuable asset most SaaS companies underuse. We build churn prediction and usage forecasting models on top of your existing product analytics, plus the KPI dashboards to track retention against, scoped to the questions your team is actually asking.

Metering and sensor data in energy come in high volume and often in formats that weren't built for analytics. We build pipelines that normalize that data, and demand forecasting on top of it once consumption reporting is something your team can trust.

Project costs, timelines, and change orders are usually tracked across separate tools per project, which makes portfolio-level reporting slow to assemble. We build pipelines that consolidate that data, so cost overruns and schedule risk show up before a project closes out.

Patient records, scheduling, and billing usually live in separate systems that don't talk to each other. We build dashboards that bring them together into KPIs your team can actually track, from bed utilization to appointment no-show rates, and forecasting models for things like patient flow once the reporting layer is solid.

Financial data comes with tight accuracy and audit requirements, and pipelines built for other industries usually cut corners here. We build ETL processes with validation and reconciliation built in, then dashboards on top so the numbers match before they reach a report.

Sales, inventory, and foot traffic data rarely sit in one place until someone builds the pipeline that connects them. We turn that into dashboards for stock levels and store performance, plus demand forecasting once you're ready to plan ahead instead of react.

Sensor data, production logs, and ERP records move at different speeds and in different formats on a factory floor. We build pipelines that bring them into one warehouse, so downtime and yield reporting reflect what's happening on the line, with predictive maintenance as a next step.

Customer behavior data piles up fast in e-commerce, but most of it never gets used past a basic sales dashboard. We build reporting on top of that data plus models for churn risk, product recommendations, and demand forecasting scoped to what you already collect.

Route data, delivery times, and fleet costs are usually tracked well enough to report on, but not well enough to predict. We build the pipelines to consolidate that data and forecasting models for demand and delivery windows that help plan capacity ahead of time.

Portfolio performance, occupancy, and pricing data often live in spreadsheets that get rebuilt every reporting cycle. We build dashboards that track these automatically, so a portfolio review doesn't start with a week of manual reconciliation.

Enrollment, attendance, and outcomes data are usually spread across systems that were never meant to talk to each other. We build reporting that brings them together, so administrators can track trends without exporting three spreadsheets to compare them.

Usage data is the most valuable asset most SaaS companies underuse. We build churn prediction and usage forecasting models on top of your existing product analytics, plus the KPI dashboards to track retention against, scoped to the questions your team is actually asking.

Metering and sensor data in energy come in high volume and often in formats that weren't built for analytics. We build pipelines that normalize that data, and demand forecasting on top of it once consumption reporting is something your team can trust.

Project costs, timelines, and change orders are usually tracked across separate tools per project, which makes portfolio-level reporting slow to assemble. We build pipelines that consolidate that data, so cost overruns and schedule risk show up before a project closes out.