An AI analyst on governed company data: start with one decision
Analyst combines the lakehouse, private models and agent sandboxes. Scope one business question, its permitted evidence and the checks a human reviewer will apply.
Relevant capabilities
- Velocity Lakehouse, Enclave and AgentBay together
- Semantic-layer and policy-governed data
- Analysis code in sandboxes and data-quality monitors
How should you compare the options?
- Are the business definitions and data owners agreed?
- Can each intended role access the required evidence?
- Which answers require human review before use?
What should you bring?
- A business question and trusted reference answer
- Approved datasets and metric definitions
- Roles, permissions and review criteria
What does a practical first workflow look like?
- Choose one decision and define what a useful answer must contain.
- Configure data permissions and semantic definitions.
- Compare outputs with trusted analysis before expanding the audience.
What should you check before expanding?
- Check calculations and evidence links.
- Test role-based differences in visibility.
- Review missing data and uncertain conclusions explicitly.
How should you evaluate the cost?
Compare the cloud bill, platform charges, operating effort and migration work for the same workload. Cloud capacity is not free. Charges and scope need agreement before paid deployment.
What needs checking before switching?
AWS and Google Cloud are available today; other deployment environments are roadmap. Preserve the existing workload and access path until compatibility, recovery and cutover checks pass.