Data, Analytics & AI

A data platform that survives contact with production

Most analytics stacks work beautifully in the demo and then quietly rot: pipelines fail silently, definitions drift between teams, and the model nobody retrained keeps scoring on last year's distribution. We build for the second year, not the launch.

That means ingestion with schema contracts and dead-letter handling, transformation as version-controlled code with tests, and a semantic layer where a metric like "active customer" is defined once instead of six times across six dashboards. Freshness and volume are monitored the same way we monitor any other production service, so a broken pipeline pages someone rather than showing yesterday's number as if it were today's. Where machine learning is genuinely the right tool, we ship it with a feature store, drift monitoring and a documented retraining path - and where it is not, we will say so before you fund it.

What We Build

A cloud data warehouse sized to your query patterns rather than to a vendor's tier chart. Batch and streaming ingestion from your operational systems, with schema contracts so an upstream change breaks the pipeline loudly instead of corrupting the table. Transformations in dbt, reviewed and tested like any other code. A semantic layer holding the definitions your finance and product teams argue about, so they stop arguing. Dashboards on top of that, and - only where it earns its keep - production machine learning with feature storage, drift monitoring and a retraining schedule somebody owns.

What You Get

Numbers your leadership team stops re-checking by hand, because the definition is versioned and the freshness is monitored. Pipelines that fail loudly and recover cleanly, with lineage you can follow from a dashboard tile back to the source column. Query costs that stay flat as volume grows, because partitioning and clustering were designed rather than defaulted. And a platform your own analysts can extend - dbt, SQL and standard warehouse tooling, documented and handed over, with no bespoke framework that only we understand.

• One definition per metric, in a versioned semantic layer
• Freshness and volume monitored like any production service
• Column-level lineage from dashboard tile back to source
• Drift monitoring and a named owner for every model