Ensemble detection
Multiple detection methods are combined, so no single brittle signal decides the call.
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AI File Detection
An ensemble classifier running on serverless infrastructure that flags AI-generated submissions, surfaced inside the case page so reviewers see the signal where they already work.
In short
AI File Detection is an ensemble classifier running on serverless infrastructure that flags AI-generated submissions inside the reviewer case page. Multiple detection methods are combined so no single brittle signal decides the call, and the compute scales with submission volume. The flag appears where reviewers already work, so they see the signal without leaving the page, and a fallback path keeps the system useful as detection methods and generation models evolve. Reviewers get a clear cue about which submissions to scrutinize, delivered in-flow, without having to trust one detector or switch tools.
BinocularsONNXAWS LambdaLWC
AutomaticFlagging
In-pageWhere reviewers work
FallbackAs methods evolve
The problem
More submissions were arriving with AI-generated content. Reviewers needed a way to know which ones to scrutinize.
They needed it without leaving the page where they already work, and without trusting a single brittle detector.
How it works
Ensemble detection, surfaced where reviewers already work.
route_to_human.pipeline Confident · auto Unsure · to a human
Incomingcases · docs
Modelconfidence
Human review
Flagged cases
Auto-resolved
Confident, cited
Serverless compute
It runs on serverless infrastructure, scaling with submission volume.
In-page signal
The flag shows up inside the case page reviewers already use, with a fallback path as detection methods change.
What shipped
AI-generated submissions get flagged automatically, where reviewers already work.
The ensemble plus a fallback path keeps it useful as detection methods evolve.
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