01
Read what the file carries
If a document arrives with a signed record of how it was made, we read it and verify the signature. That is evidence, not a guess.
You have to make decisions that are fair, that a student can appeal, and that hold up when a parent or a lawyer asks how you reached them. Here is what we can and cannot give you.
If you are looking for a tool that tells you which essays were written by AI, no such tool exists, including ours. Anyone who tells you otherwise is selling you a number they cannot stand behind.
The reason matters. A style-based detector has to be wrong sometimes, and when it is wrong it is wrong about a real student. A 2023 study in Patterns (Liang et al.) found more than half of TOEFL essays by non-native English writers were wrongly flagged by the detectors of that period. That finding is contested: a February 2026 preprint retesting three detector families, in Czech, found no systematic bias. We think the honest reading is that this is a per-tool, per-population question to be measured, not a settled fact either way, and that an institution should ask for the rate measured on writing like its own students’.
What is not contested is the arithmetic. Vanderbilt University disabled its AI detector in August 2023, saying it did not believe AI detection software was an effective tool that should be used. In that same published guidance the university did the sum itself: at the vendor’s advertised one percent false-positive rate, against roughly 75,000 papers submitted in 2022, about 750 could have been wrongly flagged in a single year. That is their arithmetic using the vendor’s own claimed figure, not a number we derived.
So we do something narrower, and we can actually defend it.
If a document arrives with a signed record of how it was made, we read it and verify the signature. That is evidence, not a guess.
A student can keep a signed chain of their drafts as they write. If they are ever questioned, that record is checkable by you, by them, and by anyone else.
Every result comes with the evidence behind it and a certificate that can be re-checked independently. A decision you can show your working for.
We serve both sides on purpose. The same tool that helps you assess a submission helps a student defend one. If a product only ever works against the student, it is not doing justice, it is doing convenience.
Most universities cannot send unpublished student work to an outside company, and they should not have to. Our verification engine holds no database, keeps no state, and writes nothing to disk. That makes it straightforward to run inside your own environment, where student work never leaves your control.
The engine and its document endpoint are built and tested, and they run today. They are not yet packaged as an on-premise product you can install yourself, and there is no hosted service to log into. An on-premise deployment is a conversation with us during the private beta, not a download. We would rather tell you that now than after a procurement process.
The endpoint takes one document at a time and accepts PDF, plain text, Markdown and HTML, so processing a cohort is a matter of running it across a folder. Theses, coursework, technical reports and engineering submissions are all just documents to it.
What you would get back for each one is a verdict, the evidence, and a certificate. What you would not get is a ranked list of suspects, because that is the artefact that causes the harm. If a department wants that, we are the wrong supplier and we will say so in the first meeting.
We do not. Plagiarism detection asks whether a submission resembles work that already exists, which requires a large licensed library of documents to compare against. We hold no such library and we produce no similarity percentage. The two tools answer different questions, and you will probably keep both.