Free guide · 8 pages
A file can pass every technical check, no nulls, no duplicates, keys unique, and still be worthless. This is the judgment that has to happen before you prompt.
8 page PDF, straight to your inbox. The whole method is on this page either way.
Why it matters
Reconciled means your systems agree with each other. They agree because they read from the same place.
Capital gets committed against these numbers. A purchase order, a production run, a stock build. The money leaves before anyone asks what the number was tested against.
The four steps
Does this number contradict itself, or does it agree with everything and still describe a business that cannot exist?
What outside my own system does this tie to? The ledger, the prior period, a physical count, a supplier invoice.
What is the rule, and what is the anchor? Give the model a specific test, never clean this.
Is the information present but messy, or genuinely missing? Only one of those is mine to fix.
Worked through
| What you notice | Compare against | Verdict and who fixes it |
|---|---|---|
| COGS exceeds revenue on a product still selling | Revenue and COGS each to the general ledger, and the prior period | Two partial samples. Source. Request the complete ledger. |
| Every extract is exactly 150 rows | Expected transaction volume, and the period's date span | Capped export. Source. Request the full extract. |
| Landed cost variance looks impossibly large | Actual landed cost composition against the budget's definition | Definitions differ. Definitional. You set the mapping. |
The trap: treating a source problem as a technical one. You cannot clean, convert or prompt your way to a number the extract never contained.
8 pages: what to notice, what to anchor it against, what AI does well and where it is blind, and who fixes each kind of finding.
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