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PEPPERParseDocs · Tathya Health
Examples

Fictional faxes, real results

Each document below is made up. The results are saved outputs of the actual service, so they open instantly. Click a finding to see the printed line behind it, then compare with what a general AI model returned for the same file.

Graded results

Pepper beside a general AI model

Held-out evaluation documents · 10 fictional documents · Not used to adjust the engine version that produced these figures. One run per system.

Same files, same taskKeyed facts found exactlySerious errorsModel usage per pageAverage time per document
Pepper Standard
gpt-6-luna, gpt-6.1-terra
223 of 234 4 $0.0009 25 s
Pepper Premium
gpt-6-luna, gpt-6.1-terra
222 of 234 4 $0.0073 39 s
A general AI model, one request
gpt-6.1-sol
222 of 234 1 $0.0177 36 s

The six example documents · 6 fictional documents · Pepper's rules were adjusted on these six, so read them as a demonstration, not a test.

Same files, same taskKeyed facts found exactlySerious errorsModel usage per pageAverage time per document
Pepper Standard
gpt-6-luna, gpt-6.1-terra
107 of 107 0 $0.0007 24 s
Pepper Premium
gpt-6-luna, gpt-6.1-terra
107 of 107 0 $0.0073 39 s
A general AI model, one request
gpt-6.1-sol
102 of 107 1 $0.0215 40 s

Counts on fictional documents from one generator, first run each, reusing none of the system's own saved work. "Serious errors" are a fact given to the wrong patient, a wrong value, date or present-or-absent marking, a value that is not printed where cited, an unreadable value reported as readable, or a required flag not raised. All 4 of Pepper Standard's serious errors on the held-out documents are one pattern: a stopped medication recorded as absent instead of as present and stopped. A manual check of those items against the pages found the drug, dose and action read correctly in each; the marking was one the output rules did not define when these runs were made, and the engine that made them was not consistent about it. The same check found that on the hardest scan Pepper read one drug name with one wrong letter (counted here as a missed fact) and Premium showed it as supported. Model usage is what the provider billed at list rates. The runs were made one after another, so much of each prompt was billed at the provider's lower rate for input it had just seen. With every such token at the ordinary input rate, the held-out figures are $0.00095, $0.00847 and $0.01833 per page. It is not an invoice and is not the customer price. These figures describe these documents; they are not a claim about real faxes.

Which engine. These runs were recorded with engine 0.2.0 and have not been repeated. The engine in service is 0.2.3. It was changed after these runs, partly because of what these documents showed: a medication reported as stopped is now recorded as present with the action stopped, a medication name the second reader spells differently is flagged, a contradiction is carried on the findings it concerns, and the second read no longer counts wording as a disagreement. For the engine in service these documents are regression cases, not untouched test documents, and the figures above do not measure it.

How the comparison is kept fair

  • Both systems get the same PDF and are asked for the same information.
  • The general model gets a plainly worded prompt a competent user would write. The exact prompt is shown.
  • Every answer is saved and graded exactly as returned. Nothing is edited by hand.
  • The expected answers are kept apart from anything a model can see.
  • Cases where the general model does well are included.

What these examples can and cannot tell you

They show how each system handled these particular fictional documents. They are not a study of real faxes, and a small number of runs can only bound run-to-run variation loosely.

The sample size and known limits are printed beside every comparative figure on each example page.