Thousands of industrial drawing PDFs held information no one could search. We built an AI agent that reads and structures the data, so teams can query the data in natural language at scale.

A large manufacturing was paying $800,000 a year to license enterprise data virtualization software for daily quality checks. We rebuilt it natively on Databricks, removing the license cost.

Water consumption data sat in disconnected sources across a property portfolio. We consolidated it into one governed model and a dashboard that flags anomalies directly.

A manufacturer was waiting up to three weeks for the right report. We replaced scattered desktop tools with a governed data platform and a self-service analytics layer.

Reports lived as template files with no real version history and no separation between test and production. We rebuilt the release process around Git, an automated pipeline, and generated documentation.

Critical production planning ran on separate spreadsheets per plant and product type, with no validation. We replaced them with one application and built error checking into the input.

An FP&A team was consolidating spreadsheets by hand to close each month. We built a governed reporting layer and a P&L dashboard covering operational and financial drivers.

Orders, schedules and the production queue lived in disconnected systems with no audit trail. We built one web application that holds all of it, with every change logged.

Scheduled pipeline triggers meant reports could pull stale data without anyone knowing. We rebuilt the orchestration so each step waits for its upstream dependencies to finish.