Real projects. Real results.

The founder was spending 3+ hours every week pulling numbers from Stripe, HubSpot, and a manual Google Sheets tracker. Every Monday morning was a copy-paste marathon just to know how the business was doing. Different people quoted different numbers for the same metric.

dbt data models that unified revenue and pipeline data into a single metrics layer, pulled product usage from an existing CSV export, then wired an automated pipeline that delivers a formatted KPI summary to Slack every Monday at 8am. Five KPIs -- WRR, pipeline coverage, trial conversion, WAU, and net revenue retention -- refreshed automatically.

Data was scattered across 8 different tools with no single source of truth. The data team spent most of their time answering ad-hoc questions instead of building anything strategic. Two AI initiatives had already stalled because nobody trusted the underlying data. The CEO wanted AI agents but the foundation was not there.

Started with an AI Readiness Diagnostic to score their current state across 5 dimensions. Then built the foundation: Terraform-managed cloud infrastructure on GCP, a dbt data warehouse with tested models covering all 8 source systems, and a CI/CD pipeline so their team could maintain and extend it without me. Everything documented in runbooks their engineers could follow.