Data foundations
Most disappointing AI projects are data problems wearing a different hat. This is the practice we have been running since 2009.
Before there was a reason to call it AI work, this was the whole business: the unglamorous architecture underneath reporting and analytics. That background is why we ask about your data before we talk about models. A retrieval system inherits every quality problem in the tables it reads, and then presents them in confident prose.
We build lakehouse and warehouse architecture, the pipelines that feed it, and the semantic models that make it queryable by both people and machines. Tested, documented, and version controlled — with lineage you can follow from a number on a dashboard back to the row that produced it.
What you get
- Lakehouse or warehouse architecture on Databricks, Snowflake, or your existing platform
- Ingestion and transformation pipelines with tests and alerting
- Semantic and metric layer so one definition of revenue exists
- Data quality monitoring: freshness, volume, distribution, schema drift
- Documented lineage from dashboard figure to source row
Call us when
- Two teams present different numbers for the same metric
- Reporting is a person with a spreadsheet and a Tuesday
- An AI initiative is blocked because nobody trusts the underlying tables
Talk to us about data foundations
Describe the situation in a paragraph. We will reply with what we would look at first and what it would cost to find out.