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.
Nephrology referral letter
A three-page referral from a primary care office: reason for referral, insurance, referring provider, medications, family history and recent results.
3 pages digitalnarrativefamily history Saved result Comparison available
Scanned laboratory report
A two-page laboratory report scanned slightly crooked, with a prior-results column and a second page that carries no patient header.
2 pages scanskewcontinuation page Saved result Comparison available
Office visit note
A two-page visit note with symptoms the patient denies, vital signs, family and social history, medication changes and a late addendum.
2 pages digitalnegativesaddendum Saved result Comparison available
Medication change and refill request
A one-page pharmacy fax, faint and sideways: one medication stopped, one started, one dose changed and one refill requested.
1 page scanlow contrastrotated Saved result Comparison available
Mixed eight-page fax packet
An eight-page fax for two relatives with the same surname: cover sheet, laboratory panels, a referral note, a reprinted result, a shared clinic log, an unlabeled page and a medication list.
8 pages faxtwo patientsunlabeled page Saved result Comparison available
Clean one-page laboratory report
A clear, digital, one-page laboratory report with ten routine results.
1 page digitalclean Saved result Comparison availablePepper 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 task | Keyed facts found exactly | Serious errors | Model usage per page | Average 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 task | Keyed facts found exactly | Serious errors | Model usage per page | Average 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.