Who is Raid AI, and why we built it
A voice cloned in seconds, a face swapped on a live call, a document forged at scale. Raid AI exists so the organisations most exposed can tell.
99.7%
Audio detection accuracy
<50ms
Audio detection latency
120+
Languages supported
Restoring trust in what you see and hear
For most of human history, hearing someone's voice was strong evidence that they were present and had said the words you heard. Generative models broke that assumption, and then did the same to photographs, video and documents.
Our mission is to restore that trust. We do it by building detection that works in the conditions enterprises actually operate in: noisy phone calls, compressed video conferences, multilingual contact centres, document queues nobody can read at volume, and air gapped government networks.
Four things that decide how this gets built
Detection that generalises
Attackers build new generators every month. Our models are trained to catch deepfakes regardless of what produced it, not only the generators we have already seen.
First place, ArA-DF 2026 Track 1
Arabic audio deepfake detection on dialects and speakers held out of training, July 2026.
Results on Codabench
Real time or nothing
A detector that flags a deepfake minutes after the damage is done is not a detector. Every model and integration is built around returning a verdict while it can still change the outcome.
6 products
Voice, image, video, documents, meetings and WhatsApp, one platform behind them.
Two SDKs
TypeScript on npm and Python on PyPI, over one REST API.
Privacy by default
Retention you control. Choose zero retention, where media is discarded once the verdict is returned, or a window you set; none of it trains a model, and enterprise tenants can keep records inside their own infrastructure.
- Cloud
- Your infrastructure
- Air gapped
Multilingual first
Most detection tools are trained almost entirely on English. Raid AI was built from day one for over 120 languages, and for Arabic it is the best deepfake detection model in the world, first on ArA-DF 2026.
Backers and partners
Notes on deepfakes
What we are seeing in detection, the attacks landing on real organisations, and what works against them. Drag to explore.
Raid AI is now on X: tag @Raidxai under any post and get the verdict
A fake spreads fastest in the replies of the post that carries it. Now the check happens there too: tag @Raidxai and the verdict lands in the same thread.
Intesa Sanpaolo loses €95M to a WhatsApp message and a cloned voice
A message from the chief executive, a call from a lawyer he knew to confirm it, and €95 million sent abroad. Neither came from the person it claimed to.
The Arup deepfake: a video call where everyone else was fake
The employee doubted the email. The video call, with the chief financial officer and several colleagues on screen, settled the doubt. None of them were real.
The $35 million voice: how a cloned director fooled a UAE bank
A bank manager heard a director he had spoken to before. Emails from a lawyer backed up the call. The voice had been cloned, and $35 million was gone.
A friend's face on a video call: the Baotou face swap fraud
He saw his friend's face and heard his voice on a video call, and sent 4.3 million yuan to help him. His friend knew nothing about it.
What deepfake detection is, and how to evaluate a detector
A detector returns a verdict and a score. The good ones also show where the manipulation is. Here is how they work on each format, and the eight questions to put to any vendor, ours included.
Raid AI places first on ArA-DF 2026 for Arabic deepfake audio
The first shared task on Arabic audio deepfake detection scored systems on dialects and speakers they never saw in training. Raid AI's audio model ranked first on that track.
How to detect deepfake audio in 2026: a complete guide
AI generated voices now clone anyone in seconds. This guide walks through the techniques, tools, and workflows you need to detect deepfake audio in 2026.
What is voice cloning and how to prevent AI voice fraud
Voice cloning can replicate a person from three seconds of audio. Here is how the technology works, why it matters, and how to defend against AI voice fraud.
Real time vs batch deepfake detection: which do you need?
Real time detection protects live calls and video feeds. Batch detection supports forensics across audio, image, and video. Most enterprises need both. Here is how to decide what to deploy where.
The 2026 deepfake threat landscape
Deepfake attacks across audio, image, and video are scaling faster than defences. This report summarises the 2026 threat landscape and the steps enterprises should take now.
Detection that sits on a research server is useless. Detection that sits on your voice stack, returning verdicts in milliseconds, is the difference between a blocked attack and a nine figure loss.
Why Raid AI exists
Reach out today
- info@raidxai.com
- Response time
- Usually within 24 hours