Simple Call, Superpower Capabilities: How Audio Intelligence Works

You send an audio file. You get a verdict. But what happens in between?

The detection pipeline is designed to be invisible to developers, a single API call that returns a confidence score in under a second. Under the hood, it's running one of the most sophisticated audio analysis systems ever built for production use.

The architecture

Our detection engine combines deep forensic analysis with advanced reasoning in a unified pipeline:

Deep Forensic Detection

At the core is a proprietary forensic model trained on more than 5 million audio samples spanning wide range of different TTS and voice synthesis techniques. This isn't a simple binary classifier - it's a forensic engine that has learned the subtle fingerprints left behind by every major (and minor) synthesis method in existence.

It analyzes audio at multiple resolutions simultaneously -from tiny frame-by-frame artifacts to broad patterns across the whole clip. That multi-scale approach catches everything from the clumsy seams in stitched-together speech to the unnaturally smooth voice produced by neural generators.

What makes the detection uniquely powerful:

It doesn't read the audio frame by frame. It builds a rich representation that links sound and time together, so it can spot dependencies across both dimensions at once.

Trained across wide range of synthesis techniques, it recognizes the characteristic "signature" each generation method leaves behind -artifacts no human can hear but a model can't miss.

It learns directly from raw audio, capturing the fundamental structure of real human speech -breath patterns, prosody, the way words actually flow into each other. That makes it extremely difficult for any synthesis method to fool.

Forensic Reasoning & Intelligence (in the playground today)

The playground layers a forensic reasoning engine on top of the detector - spectrogram analysis and AI-written explanations for every verdict. It:

• Reads the spectrogram visually, flagging anomalous regions and spectral patterns that point to manipulation.
• Explains what it found in plain language, so non-experts can act on the result without a signal-processing degree.
• Calibrates the confidence score against the full audio context -noise, codec, recording quality -so the probability you get out actually means something.

The detection model finds the evidence; the reasoning layer understands and explains it. Both run live in the playground - try them on your own audio.

Why one API call is enough

We obsessed over developer experience. The entire pipeline is engineered to feel instant -under a second from request to verdict -so you can run it inline on every audio stream without your users ever noticing.

Zero configuration. Send audio in any common format and we handle normalization, segmentation, and analysis automatically.

Built for real-time. The pipeline is tuned end-to-end so detection never becomes a bottleneck in your voice flow.

Long audio, no problem. Files of any length are analyzed intelligently, producing a single aggregated verdict you can act on immediately.

The result: developers get a simple POST endpoint that accepts audio files and returns structured JSON. No ML expertise required. No GPU provisioning. No model management.

The accuracy question

On standard benchmarks (e.g ASVspoof, In-the-Wild), our model achieves state-of-the-art equal error rate results. But benchmarks don't tell the whole story.

Real-world audio comes with background noise, compression artifacts, codec distortions, and recording conditions that benchmarks don't capture. Our model is trained on a massively diverse dataset:

• 5,000,000+ audio samples across real and synthetic speech
• A wide range of synthesis techniques covering every major TTS engine, voice cloning tool, and generator
• Every common codec and bitrate
• Real-world noise conditions
• Cross-lingual samples across 50+ languages

We continuously red-team our model against the latest synthesis systems and ship updates monthly.

Getting started

Built for fast integration and reliable results. Drop it into any stack, send audio, and get a verdict in under a second -accurate enough to act on, simple enough to ship today.