DATA FOUNDATIONS FOR AI ANALYTICS
AI analytics on data that's trusted and accurate.
A workshop, implementation, and advisory for organizations whose AI analytics pilots stalled on trust.
The problem
Simply connecting AI to the warehouse didn't return accurate numbers.
You connected AI to your data warehouse and thought you were done when the chatbot returned a confident answer, quickly. But when the head of finance tells you it’s incorrect in your weekly business review, your self-service analytics immediately stalls and your org swirls.
- 01Metric definitions no one has agreed to
- 02Data contracts that exist but aren't enforced
- 03Context scattered across sources and people
- 04No agreed standard for when an answer is good enough to ship
Our approach
Rebuild the data foundation, quickly, and keep leaders in the loop.
- 1
Diagnose
Audit lineage, usage, observability, and contract coverage to show where the foundation breaks and whether to rebuild or extend.
- 2Human bookend
Align
Leaders agree on key metrics and their definitions. This is the step most teams skip.
- 3
Generate
Data contracts drive generated pipelines and certified metric views, so a definition change is a contract edit, not a project.
- 4
Route and govern
A driver agent routes questions to scoped domain spaces using generated context files, with permissions and refusal boundaries enforced.
- 5Human bookend
Decide
Leaders set accuracy thresholds by metric and decide what the agent must refuse.
Proof
Built for a leading digital health company after three warehouse remodels failed.
3 months
To rebuild the enterprise data model on Databricks
Quarter → <1 day
A data contract change that once took a quarter of engineering work now ships in less than a day
1 agentic entry point
A driver agent routes questions to domain Genie spaces, with routing held in context files instead of code
>90% Accuracy
Answer accuracy on certified-tier metrics
Who this is for
Leaders who need answers they can believe.
- Databricks or AWS customers with a Genie or AI analytics pilot that stalled on trust or accuracy
- Teams with sensitive or regulated data that need permissions, refusal boundaries, and audit trails built in from the start
- Data leaders whose teams are buried in ad hoc requests and want self-service that leadership will actually believe
Next step
Book a 30-minute fit call.
If it's a fit, we send the intake survey and access checklist, and the workshop can start within three weeks.