Sample outputs
Real files from real runs.
Download what Analyze actually produced, exactly as it came out. Each sample says which AI did what and where the data came from.
01Team report
Development since 2005: four regions, ranked
From the example prompt Global Development, Region by Region. Claude was the Operator; Gemini and Mistral each profiled two regions as teammates; ChatGPT reviewed the report. Data from the World Bank and the UN World Population Prospects, fetched on October 18, 2026.
Published as the Operator wrote it. The Reviewer raised five points; one was checked independently and confirmed, and none were sent back for revision.
- Interactive page114 KB
Opens in your browser, with live charts and a Sources page.
- PDF0.9 MB
12 pages, with numbered citations and a sources appendix.
- PowerPoint1.5 MB
32 slides with the report’s charts and tables.
- Word1.0 MB
Editable, with the same headings and tables.
- Excel workbook0.5 MB
The data behind the report.
02Model
What can I afford? A mortgage model
From the example prompt What Can I Afford? A Mortgage Model. Claude built it on FRED's 30-year mortgage rate and the national median sales price, read on October 18, 2026. The download holds the baseline and a second scenario with a larger down payment.
An illustration of what a model is, not financial advice.
- Model download (zip)81 KB
The recipe, the FRED readings it used, each scenario’s results, and a script that runs it with Python.
03Salesforce
An audit, a troubleshooting handoff and a change-impact model
From a demo org built on Salesforce's public Coral Cloud sample app, extended with demo automation. Claude was the Operator, working from the org's uploaded metadata and Salesforce documentation. For the audit, a CLI agent added read-only org evidence: SOQL and Tooling API results gathered with the CLI kit, from the same demo org.
The model runs on its own: unzip it, install pandas, run python run_model.py, and it reproduces the app's results.
- Workspace audit (PDF)0.5 MB
13 pages: risks, conflicts between automations, and what to fix first, with each figure labeled as metadata, org evidence or documentation.
- Troubleshooting handoff (PDF)0.4 MB
8 pages: why a cancellation fails with “Credits over $500 need a reason”, the failing step, and the fix as tasks, not patches.
- Change-impact model (zip)108 KB
What renaming Experience__c.Price__c would touch: the recipe, the reference map it reads, and a script that runs it with Python.
More about what Analyze does on the Analyze tour, and for Salesforce orgs on the Salesforce page.