Synthetic example · no customer data
Sales total
Before: three Revenue values. Operation: sum Revenue. After: 400; verify every contributing cell.
120 + 180 + 100 = 400
synthetic-sales.csv · B2 + B3 + B4
CSV ↓ synthetic-sales.csv
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See the interface. Reproduce the workflow.
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Synthetic example · no customer data
Reproduce a verified result
Before: two separate CSV files
CustomerId,Revenue C001,120 C002,180 C001,100
CustomerId,Segment C001,Retail C002,Wholesale
Operation: match CustomerId, approve the relationship, group Revenue by Segment and calculate Sum.
After: Retail 220 · Wholesale 180 · Total 400
Source check: sales B2 + B4 = 220; B3 = 180. All 3 records match unique customer keys. The report includes cell evidence and a source fingerprint.
Other reproducible checks: Revenue 400; Cost 240; Actual 400 versus Target 390 gives +10; Stock totals 22. Values are unitless synthetic observations, not business performance claims.
Synthetic example · no customer data
Inventory quantity
Before: three item quantities. Operation: sum Stock and review the smallest quantity. After: 22 in total; P002 has 3. No reorder is made.
8 + 3 + 11 = 22
synthetic-inventory.csv · B2 + B3 + B4
CSV ↓ synthetic-inventory.csvSynthetic example · no customer data
Actual versus target
Before: Actual and Target for three periods. Operation: compare totals. After: absolute variance +10. This does not imply a forecast.
400 − 390 = +10
synthetic-budget.csv · SUM(B2:B4) − SUM(C2:C4)
CSV ↓ synthetic-budget.csv
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