We ship production systems, not presentations.
Pie Data was built for one specific gap: organizations that have accumulated data but lack the in-house depth to operationalize it at scale. We close that gap with working code in production.
Rigor over theater. Code over decks.
Every engagement ends with a production-ready system — a deployed model, a running pipeline, an automated decision loop. Consulting deliverables that don't change a process are overhead we refuse to bill for.
That means we scope tightly, instrument everything, and measure outcome impact — margin lift, churn reduction, cycle-time compression — before we call an engagement complete.
A four-phase system for measurable outcomes
Audit and instrument first: we map your existing data flows, identify gaps in collection and quality, and baseline the metrics the engagement will move.
Engineer, model, automate: pipelines are built to production spec before any model is trained. Models are deployed into decision loops, not notebooks. Automation replaces the manual step.
Instrument the outcome: every system ships with monitoring, alerting, and a clear metric owners can track without our involvement.
Specific expertise, not generalist coverage
SaaS & Subscription
Financial Services
Growth-Stage Marketing
Churn prediction, expansion revenue modeling, product-led growth instrumentation, and activation funnel automation for recurring-revenue businesses.
Risk scoring pipelines, fraud detection models, regulatory data infrastructure, and automated credit decisioning built to compliance and performance standards.
Multi-touch attribution, paid media performance pipelines, LTV modeling, and automated audience segmentation for teams scaling past manual reporting.