AI-guided workbenches, simulations, games, and odd tools
Learning mode
Explain the analytics in plain English so users understand beta, factor importance, confidence, regime breaks, and model limitations.
Guided intake
The workshop guide uses these intake points to ask sharper questions before producing structured output.
Goal
What decision should model classroom support?
Inputs
Which dataset, ticker, scenario, or assumption set should be trusted?
Constraint
What risk limit, horizon, or caveat should shape the output?
Output contract
Recommendation
What the workflow suggests and why it is not automatic advice.
Evidence
The metrics, chart movement, or model signal supporting the view.
Caveat
The assumption, missing data, or model risk that could change the result.
Next step
The most useful follow-up action before exporting or sharing.
Concept cards
12
Plain-language explanations for key analytics
Caveat prompts
9
Questions that prevent overconfident model use
Read time
6m
Designed for fast onboarding before analysis
Open any workflow and ask the agent to explain a metric in classroom terms.