ShellNewHandler VS Fileverity

Fileverity's answer:

Evidence, not just a label. Edited regions are located to the pixel, and flagged passages are referenced by page or paragraph.

One check for many generators. It examines the content itself across the major model families, so you don't need a separate tool for each vendor's watermark.

Images and documents in one place. Check claim photos, receipts and documents in a single workflow, in five languages.

Reports you can file. Fingerprinted, timestamped and sealed reports that anyone can verify, even without Fileverity.

Privacy by design. Files are never retained or used for training, and on-premises deployment is available.

Pay per report. Start free, with no subscription.

Fileverity's answer:

Fileverity checks both images and documents, and every finding comes with a record that holds up when someone questions it. Instead of a single score, it shows where the evidence is: the edited region in a photo, or the AI-written passages in a document. It also reads Content Credentials (C2PA), watermarks, AI labels and file metadata in the same check. Each report is bound to the exact file by its SHA-256 fingerprint, timestamped by DigiCert under RFC 3161, sealed as a PDF and verifiable on a public page. Files are deleted after analysis and never used for training, and Enterprise customers can run Fileverity fully offline inside their own infrastructure.

Fileverity's answer:

Teams that have to decide whether a file can be trusted, and keep a record of why. That includes insurance claims handlers and anti-fraud (SIU) teams reviewing claim photos and documents, finance and expense teams checking receipts and invoices, legal and compliance teams, newsrooms and fact-checkers verifying images, and marketplaces and platforms reviewing user submissions. Individuals can also use the free checks to see whether an image is AI-generated or edited.

Fileverity's answer:

Generative AI has made fabricated evidence look convincing. A fake damage photo, an AI-edited receipt or an AI-written claim letter no longer gives itself away through obvious mistakes. Most detection tools answer with a single score and keep the uploaded files, which doesn't work for teams whose decisions may later be challenged. Fileverity was built in Toronto by Entogo Inc. to close that gap. It runs on detection models built in-house, shows the evidence behind every finding, publishes its error rates, and issues a report that can be verified independently. The aim is to support human reviewers, not replace them.

Fileverity's answer:

Proprietary detection models, including FVDOC V3 for AI-written documents

Image forensics for local edits, compression history and pixel consistency

C2PA Content Credentials and AI-label reading

SHA-256 file fingerprinting

RFC 3161 trusted timestamps from DigiCert

Sealed PDF reports with JSON data