How PerfectPaper keeps your research out of model training

PerfectPaper never uses your content to train a model. Not the paper, not the model’s reasoning, not its reply, not the derived metadata. Three layers hold that guarantee up: PerfectPaper’s own code, which trains nothing; the contract terms binding the model providers that process your text; and a published list of every processor that receives any part of your manuscript.

That sentence is deliberately unqualified. The rest of this page explains what holds it up, because a guarantee a reader cannot inspect is only a slogan.

Layer one: PerfectPaper’s own code

PerfectPaper trains no models and fine-tunes none, so no customer manuscript is training data by construction.

The evaluation corpus used to test the review engine is worth naming precisely, because this is where products most often keep customer work quietly. PerfectPaper’s corpus contains eight synthetic papers written for the test harness with deliberately planted flaws, and no customer manuscript at all. When the engine’s accuracy is measured, it is measured against papers written for that purpose.

Layer two: contract terms, not a request flag

Enforcement is contractual rather than technical, and that distinction is worth stating plainly rather than glossing.

PerfectPaper does not send a per-request do-not-train flag to model providers. The commitment rests on the commercial and cloud agreements under which those providers process the traffic. This is the ordinary state of affairs for products built on hosted models — most provider APIs do not expose such a flag in the first place — but it is rarely said out loud, and a research office is entitled to know which kind of guarantee it is being offered.

Layer three: a published processor list

Every third party that receives any part of your manuscript is named on the subprocessor page, along with what it receives.

Naming them is what makes the first two layers checkable. A privacy promise attached to an unnamed supply chain cannot be verified by the reader, and an institution evaluating a vendor is right to treat one as unverified. The list also distinguishes scope: the review models receive manuscript text, while scholarly search and embedding services receive a title and abstract only.

Why the phrase is worth reading carefully anywhere you see it

A commitment not to train on customer data is compatible with several things researchers usually mean to rule out. It does not by itself rule out retaining content for abuse monitoring, sampling content for human review, or applying a different default to a different plan.

When comparing tools, read for the specific claim rather than for the phrase. Ask what is retained, for how long, who may read it, and whether the answer changes with your plan. The procurement checklist puts those questions in a form you can send to a vendor, and is it safe to upload a manuscript to ChatGPT works through the same three checks for general-purpose chat products.

What would change this

If a provider changed the terms under which it processes PerfectPaper traffic, the honest response would be to change this page and the subprocessor page rather than to leave the sentence standing.

That is the maintenance commitment attached to publishing an unqualified claim. It is also why the processing details are derived from the running configuration rather than typed into a template — a page that describes its own deployment cannot drift away from it silently. Where your manuscript is processed sets out the route that mechanism reports.

Review my manuscript

Frequently asked questions

Does PerfectPaper train on my manuscript?

No. PerfectPaper trains no models and fine-tunes none, and no customer manuscript appears in its evaluation corpus. The corpus is eight synthetic papers written for the test harness.

How is the no-training commitment enforced?

Through two things: PerfectPaper’s own code, which trains on nothing, and the commercial and cloud contract terms binding the model providers that process the traffic. It is a contractual guarantee rather than a per-request switch, and PerfectPaper states that openly.

Which AI providers process my paper?

Every provider is named on the subprocessor page along with what it receives. The review models receive manuscript text; scholarly search and embedding services receive a title and abstract only.

Is my data retained after the review finishes?

Your manuscript and review remain in your account until you delete them, so that you can return to the feedback. Deletion is available in the account and through a self-service privacy rights path.

Do you send a no-training flag with each API request?

No, and few provider APIs offer one. The commitment rests on contract terms rather than on a per-request setting, which is why PerfectPaper publishes the provider list rather than pointing at a toggle.