We prepare the data layer AI depends on: pipelines, quality checks, semantic layers, document stores and governance, and classic machine learning (forecasting, scoring, anomaly detection) where it beats an LLM.
Typical use cases
Challenge. Useful data sits in silos with no ownership or quality guarantees.
What we build. A lakehouse with curated, documented data products and access controls for AI use.
Challenge. Business users wait days for simple reports.
What we build. A governed natural-language analytics assistant on top of a semantic layer, with query verification.
Challenge. Demand, churn or fraud are handled with spreadsheets and rules.
What we build. Classic ML models with explainability, deployed into existing workflows.
Examples
Use cases with Data for AI.
Next step
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