On 240 fabricated month-end reports from a fictional SAP system, with every correct value recorded in advance, a language model parsed each one while a second system learned the repeating formats and wrote ordinary code for them. 184 of 240 ended up on that code, at an estimated two fifths of the cost.
At Leena’s retirement party, the finance manager is quietly doing the arithmetic. Leena has been copy-pasting twenty SAP reports into spreadsheets every month for twenty years, and she will be missed for the pulla she baked and, now, for knowing what all those columns mean.
Those reports arrive as plain text exports.
The company can rebuild the system, which is a seven-figure project, or it can put a language model on the reports, which works immediately but costs money on every document. This case takes a third route.
A language model reads each report and turns it into a structured record. But after eight consistent examples of the same report type, a second system writes a simple piece of code that can read that format on its own. That code goes live only after it matches the model’s output on every earlier document of that type, and if a later document does not come out right, the document goes back to the model.
By the end of the year, about seventy-seven per cent of the documents had finished on that code. The run cost around eight and a half dollars, against nearly twenty-two for putting every document through a model. That saving grows with volume, because once the code exists for a format, a routine document costs nothing. Most companies will not be dealing with only twenty SAP reports.
Although the real comparison, of course, would have been replacing the system with a new one that in time becomes the next legacy system. Everything here is invented. The method works on real exports, but how much of your stream ends up on code instead of on the model depends on what your own reports look like.
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