The work it expands
The agent handles exploratory and recurring questions that often sit between a dashboard and an ad hoc request.
- Compare periods and segments
- Investigate changes and possible causes
- Prepare a readout for a decision
AI data analyst
An AI analyst that works with your business rules
Rumbleo helps teams explore internal data in natural language without turning every question into a new report request. The data team retains definitions and control while the agent makes that foundation reusable.
Questions, clarifications, and follow-ups in one conversation
Metrics and dimensions from the semantic model
Validated, read-only analytical queries
What it needs
A reliable answer starts with shared definitions and an analytical connection prepared by the organization.
- Documented metrics and dimensions
- Data available to the tenant
- Enough context or a clarification from the user
What stays controlled
Agent autonomy does not replace access policies or grant direct control over the database.
- Identity and permissions
- Row and time limits
- Audit and usage controls
Questions suited to an AI data analyst
The value appears when a question requires exploration and explanation, not merely retrieving one number.
Finance
“Why did margin fall versus last month?”
Break down the variance by business unit, category, or component and validate hypotheses in sequence.
Operations
“Where is the increase in incidents concentrated?”
Compare sites, suppliers, products, or periods through the dimensions available in the model.
Growth
“Which cohorts explain the retention change?”
Explore acquisition, behavior, and change over time without requesting a new view for every cut.
Continue evaluating Rumbleo
Frequently asked questions
Does an AI data analyst replace the data team?
No. It expands access to analysis and reduces repetitive work while the data team retains responsibility for definitions, quality, and governance.
Can it invent a metric?
Rumbleo uses the tenant's semantic model as its source of truth. If a definition is missing or ambiguous, the system should ask for context before continuing.
Does the user see the SQL?
No. The end-user experience presents the analysis while technical queries remain in the internal traceability layer when applicable.
A demo built around a decision
Test a real analytical use case
Bring a question that currently joins the data team's queue. We will examine the context, metrics, and controls required to answer it well.
We will start with a real question from your team