CSV: orders, invoices, warehouse.
Technical demonstration · synthetic data
Data reconciliation
between exports or systems
When orders, invoices and warehouse exports disagree, you need to find the affected records. This example lets you inspect each discrepancy and its source.
Try a sample comparison ↓Compares order, invoice and warehouse exports using explicit rules. The result identifies the record, discrepancy and recommended next action.
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Browser-side processingData is processed locally in the browser and is not sent to a server.
Matching keys and deterministic field comparison.
Missing records, duplicates and inconsistent amounts, quantities and statuses.
Record, expected and actual values, evidence and CSV report.
Fixture: 120 records, 18 agreed discrepancies; export matches the result.
Controlled change: invoice INV-2001 amount increases by 12.50 while order ORD-1001 amount stays unchanged.
Data sources
Run validation
You can use the built-in dataset or upload a matching set of three files.
The result will appear here
The built-in set contains 120 records and intentionally added edge cases.
What this example demonstrates
From input file to acceptance criterion
The demonstration rules are examples. For a real scope, they are defined from the company's processes and data.
3 CSV exports
Orders, invoices and inventory issues with shared identifiers.
Explicit comparisons
Uniqueness, completeness and consistency of quantities, amounts, statuses, dates and SKUs.
Exception report
Filters, priority, reason, comparison values and next action.
Measurable result
A stable test-set result, detection of every seeded error and a matching CSV export.
Implementation scope
Validator for a specific process
2–3 exports → agreed matching rules → validator → discrepancy report → acceptance on an agreed sample.
The scope can run locally, cyclically or as part of an existing process depending on data and system constraints.