Comparison
Detection guesses at authorship. Verification checks the work.
One produces a percentage nobody can defend in a dispute. The other produces findings, a score and a name. They get sold as the same category and they are not.
Same input, different instruments.
AI detection
Statistical guess about how text was produced.
Verification
Inspection of what the work actually contains.
Question answered
Was this written by a model?
Is this correct, sourced, structured and safe to ship?
Evidence offered
A probability with no inspectable rule behind it.
Named findings with severity and a location.
Reliability
Shifts with editing, translation and model version. False positives are routine on non-native writing.
Deterministic: the same input produces the same findings.
Defensible in a dispute
Poorly. A percentage is not a proof of authorship.
Yes — the finding states what it observed.
Useful for a decision
Rarely. Nothing follows from knowing a draft was assisted.
Directly: fix, escalate or ship.
Harm when wrong
Someone is accused of something they did not do.
A finding is dismissed with a reason recorded.
Why detection keeps being bought anyway
It answers a question institutions were already asking — is this student's, this applicant's, this journalist's own work — and a percentage feels like an answer. The trouble is that a probability is not evidence, and the consequences of being wrong land on a person rather than on the work.
What to ask instead
For most teams the real question is whether the output is good enough and safe enough to ship, which is a verification question with a checkable answer.
For the few genuinely authorship-sensitive cases, the honest controls are process ones: a draft history, a conversation, a reference check. Not a detector score.
Run it yourself
Find out what your AI output is hiding.
The console is free, needs no account and uploads nothing. See the findings first, then decide whether you want a specialist to sign it off.