One detector, many decisions.
Whoever has to decide whether a recording is real, a fraud analyst, an editor, an investigator, a developer, reaches the same model. These are the ways teams put an AI voice verdict to work.
Stop cloned-voice fraud before the money moves
Cloned voices now authorize wire transfers and pass phone checks. Fraud and security teams at banks and call centers use the detector to screen flagged recordings for executive-impersonation (CEO) fraud, vishing, and family-emergency scams, then keep a citable verdict for the case file. It fits a review workflow, not a live switchboard: we analyze recordings after the fact and integrate through the API, never by intercepting live calls. See the fraud detection use case.
Newsrooms and journalismVerify audio before you publish
Fabricated "leaked" clips and manipulated candidate audio move faster than a correction. Newsrooms verify suspicious recordings before publication and cite the verdict, with a published methodology that holds up in editorial review. The detector reports confidence honestly and says when it cannot tell, which is exactly the caution an editor needs on deadline. See the newsrooms use case and our note on deepfakes in elections.
Legal and investigationsAuthenticate audio for a case
Recordings enter disputes, custody matters, and investigations already suspect. A saved verdict provides a probability, a confidence level, a methodology version stamp, and a one-way fingerprint under a permanent citation URL that an opposing expert can reproduce. It is a documented analysis, not a ruling on admissibility. See the legal and investigations use case.
DevelopersBuild the verdict into your stack
The deepfake voice detection API returns the same JSON verdict as the public detector, with webhooks for bulk and async jobs, so screening runs inside your own tooling. For the product framing, see deepfake voice detection; to identify a suspected generator, start from the detect pages.
The same model behind every use case
Whatever the surface, the detector reads the acoustic fingerprint of a clip, the spectral, prosodic, and timing traces synthesis leaves behind, and returns a probability, a confidence level, and the source model where recognized, usually in under half a second. It works on cloned and stock voices alike, and returns an honest "unknown synthesis" when it cannot attribute the source.
Drop or POST a clip
Use the public detector, the browser extension, or the API. MP3, WAV, M4A, WebM, or a video's audio track.
Read the verdict
Probability, confidence, and the named model in about half a second, with honest confidence on compressed audio.
Save and act
Keep a permanent citation URL and fingerprint for a case file, a story, or an audit trail.
Common questions
Who uses AI voice detection?
Is it real-time call monitoring?
Can I try it before integrating?
Put a verdict to work.
Free for a single verdict. API and volume on Starter and above.