DetectifAI builds foundational models to detect audio deepfakes — and the trust layer that puts them to work. Verify the authenticity of voice notes, calls and recordings, and prove that a live speaker is who they claim to be.
Sequential — all must pass
Claim checked against a voiceprint
Falls back on an uncertain score
Challenge and transcription
Every call centre, every KYC flow, every "it's me, approve the transfer" voice note rests on an assumption that stopped being true: that a voice is hard to fake. Open cloning models reproduce timbre, cadence and accent from a short sample, and the output survives compression, phone codecs and background noise well enough to pass a human listener.
The defensive side has not kept up. Most teams either bolt on a single deepfake classifier and treat its number as truth, or they check identity and never check authenticity at all — which means a perfect clone of an enrolled user walks straight through the front door.
A convincing clone costs nothing and takes under a minute. The attacker does not need to be sophisticated.
Before anyone reaches for a generator, they simply re-submit audio the real user already produced.
Every detector fails on some generator, some codec, some room. A single classifier is a single point of failure.
Detection is not a model, it's a decision — and a decision needs a system around it. The trust layer scores every sample, recognises when that score isn't confident enough to act on, and falls through to another detection layer rather than guessing. One layer answers, the answer is recorded, and the caller gets a decision instead of a raw number.
A single layer's score decides the outcome, and the audit log records which one. No silent averaging, no blended number hiding a disagreement.
We characterise where each layer degrades — codec, compression, room, generator — and route around it, rather than trusting one model everywhere.
Layers are configuration, not architecture. Adding, reordering or removing a detector is an environment change, never a rewrite.
Fail-open with a logged warning keeps a call centre moving; fail-closed makes authenticity a hard gate. Same code, one setting.
Gates run sequentially and short-circuit: a failure at gate one means the speaker model never runs, and a speaker mismatch means we never spend an authenticity call.
One recording of a ~15 second passage, or five short sentences merged into a single template. Each sample passes the same liveness gate before it is allowed to shape the voiceprint, so nobody enrolls a clone.
The console can submit a pre-recorded or synthetic file through the exact pipeline a live microphone uses. It's the fastest way to watch gates one and three do their job — and it's built into the demo.
The console runs the real pipeline — the same gates and the same trust layer described on this page. Record a passage, verify against it, then inject a file and watch the layer reject it.