Who behaves differently?
Explore clusters by frequency, value, and behavior.
Your analyst + data scientist
Ask a question. Work through the answer.
Your AI analyst explains the numbers. Your data scientist prepares data, builds forecasts, and tests models. Review their work, ask a follow-up, and share useful results.
Business questionsForecasts and patternsReviewable resultsReports and datasets
HorizonMay–June
ReviewRange + method
ObservedForecastPlanning range
Plan for a range, not one number.Compare a baseline, review assumptions, and test an alternative.
How it helps
Use the AI workspace to investigate performance, prepare data, forecast trends, and explore patterns. Review the methods, findings, and output files. Authorized users can publish result datasets and schedule approved analysis.
✓ Forecasting✓ Customer segmentation✓ Regression & classification✓ Anomaly detection✓ Statistical exploration✓ Data preparation
Who this is for
- Business teams with questions beyond a dashboard
- Analysts turning recurring work into reusable analysis
- Data teams governing inputs, methods, and published results
From curiosity to action
Work from the question to a useful result.
Start with a business question. Keep asking as the analysis takes shape.
1
Frame the question
Clarify the goal, select accessible data, and agree on scope before the work begins.
2
Do the analysis
Explore patterns, prepare data, and run the statistical or predictive work the question needs.
3
Review the evidence
Inspect findings, assumptions, validation, and the executed notebook. Ask for a different approach.
4
Put it to work
Publish an approved result as a dataset, build reports and dashboards, or set up an approved recurring analysis.
Two complementary capabilities
Understand the numbers and explore what comes next.
Move naturally between exploring a metric and doing the deeper work behind a decision.
01
Your analyst connects the numbers to the question.
Explore performance in plain language. Compare periods, investigate the contributors to a change, and turn the findings into clear charts and a business narrative.
- ✓Investigate trends, comparisons, and revenue drivers
- ✓Create charts, tables, pivots, and dashboard drafts
- ✓Carry context into follow-up questions
02
Your data scientist takes the work further.
Prepare data and run analytical code for forecasting, statistical analysis, segmentation, regression, classification, and anomaly detection. Review the chosen approach and its limitations alongside the output.
- ✓Inspectable Python notebooks and result files
- ✓Validation and assumptions alongside findings
- ✓Refine an analysis through follow-up revisions
03
Keep the analysis. Build on the result.
Continue a saved analysis, compare revisions, and promote useful outputs into governed datasets. Review the target and update method before changing existing data.
- ✓Create, replace, append, or update by key with appropriate permissions
- ✓Keep reports connected to the same dataset identity
- ✓Approve a repeatable method for scheduled refresh
A wider range of questions
Choose the analysis your question needs.
Forecasting
Explore future demand or revenue, compare against a baseline, and review uncertainty.
Customer segmentation
Find meaningful groups and translate their profiles into actions your team can test.
Regression & classification
Model relationships and outcomes with appropriate features, validation, and limitations.
Anomaly detection
Surface unusual records or changes and investigate the business context.
Statistical exploration
Examine distributions, relationships, data quality, and the evidence behind a hypothesis.
Data preparation
Combine authorized inputs, transform fields, and produce reusable analytical datasets.
Different questions need different views
Go beyond a simple trend chart.
Use the visual that makes the pattern clear—then inspect the method and the underlying data.
What explains the change?
Separate the factors that add to or reduce a result.
Who keeps coming back?
M0M1M2M3M4M5
Jan100%81%74%68%65%63%
Feb100%85%79%75%72%
Mar100%87%84%80%
Apr100%86%82%
May100%89%
Jun100%
Compare customer groups over time, beyond an overall average.
Work that travels
From analysis to the next team meeting.
Use the result where decisions happen. Publish a dataset for ongoing reporting, build a dashboard, or turn supported findings into a branded storyboard.
- ✓Charts, reports, dashboards, and pivots
- ✓Inspectable notebooks and downloadable result files
- ✓PowerPoint, PDF, and interactive storyboard exports
Enterprise foundations
Review the work before you share it.
Your data, users, and approval rules remain part of the workflow.
Authorized data
Dataset access, tenant boundaries, and field controls govern which inputs are available.
Reviewable work
Keep the analysis, outputs, revisions, and execution history available for inspection.
Controlled publishing
Review dataset updates and approve a method before enabling recurring execution.
Your analytics platform
Use governed dashboards and reports internally, in customer portals, or embedded in your product.
Ready when you are
Bring your toughest data question.
See how your AI analyst and data scientist can turn it into findings your team can review and use.
FAQ
Frequently asked questions
What does the InfuseBI AI data scientist do?+
The data scientist prepares data and executes analytical code for statistical exploration, forecasting, regression, classification, clustering, and anomaly detection. Results include inspectable notebooks and output files. The appropriate method depends on the question, available data, and configured runtime capabilities.
How is it different from asking for a chart?+
A chart describes a view of the data. A data science task can prepare new data, test an analytical approach, compare models, and produce reusable results. The analyst helps translate the findings into business explanations and visualizations.
Can I inspect and refine the work?+
Yes. You can review the analysis, its notebook and outputs, and request a follow-up revision. Check assumptions and validation before relying on a model or publishing the result.
Can an analysis become a dataset or run on a schedule?+
Yes. Authorized users can publish results as new datasets or review changes to existing datasets. Approved analysis methods can be configured for recurring refresh, with permission checks and execution history.
Is the interactive example running on my data?+
No. The website walkthrough uses prepared, synthetic examples and does not call a live AI model. Your question stays in your browser and selects a sample scenario. Request a demo to discuss your own data and workflow.
Do we still get dashboards, reports, and embedded analytics?+
Yes. The analyst and data scientist sit within InfuseBI’s governed analytics platform, alongside dashboards, reports, pivots, exports, customer workspaces, and embedding.