Why this exists.
Every fraud vendor told us they had “AI-powered detection.” None of them could explain a single decision. So we built one that can.
The Problem We Saw
We spent years watching fraud vendors sell ML models that couldn't explain their own decisions. A score of 73 meant nothing if you couldn't trace it back to specific signals. When a regulator asks “why did you block this user?” — “the model said so” is not an answer.
We asked a different question: what if every fraud decision was deterministic? Same inputs, same output, every time. No training data drift. No unexplainable confidence scores. Just physics, statistics, and a full audit trail.
That question led us to hardware-level signal collection — crystal oscillator drift, GPU shader timing, audio DAC fingerprinting — signals that exist below the JavaScript API layer and resist spoofing by design. The result is a fraud engine where every decision can be traced to specific physical measurements on a specific device.
Technical Timeline
Engineering Principles
Determinism Over Prediction
We chose auditable math over ML black boxes. Our customers need to explain every decision to regulators, auditors, and end users. Predictions are guesses. Deterministic scoring is reproducible.
Adversarial Thinking
We assume every signal can be spoofed. Our architecture is designed around what survives adversarial pressure, not what looks impressive in a controlled demo.
False Positives Are Failures
A false positive costs your customer a sale and your brand their trust. We'd rather flag uncertainty than produce a confident wrong answer.
Show Your Work
Every risk score includes the full signal chain. If we can't explain a decision, we don't make it. Our benchmarks are open-source. Our methodology is published.
The engine is already running.
Go to the homepage. Scroll to the live proof section. Inspect the API response in DevTools. Then decide if you need this.