Unify every source into a governed, real-time customer model — then run experiments, build features, and ship ML without rebuilding pipelines.
Every team rebuilds the same joins, metrics disagree across dashboards, and a new feature takes weeks of plumbing before a model can use it.
Custom ETL breaks on every schema change and eats engineering time.
Managed real-time ingestion and identity resolution remove the plumbing.
“Active customer” means something different in every report.
One governed semantic model defines every entity and metric once.
Standing up features for a model takes weeks of one-off pipelines.
A reusable feature store and ML canvas turn weeks into minutes.
Resolve every source into one canonical, real-time customer schema.
Stitch devices, channels, and time into one persistent profile.
Define features once and reuse them across every model and team.
Build, train, and ship models on live data without leaving the platform.
Column-level lineage and access controls on every field.
Sync bi-directionally with Snowflake, BigQuery, and your stack.
See a governed customer model, feature store, and ML canvas running on real-time data.