How Tracio Achieves
99.5% Accuracy
A complete technical breakdown of the architecture, signals, and behavioral analysis behind every identification. Written for CTOs and engineers who need to understand exactly what they're integrating.
THE PROBLEM
Why Free Solutions Fail
Open-source fingerprinting libraries have a fundamental flaw: once the code is public, attackers can read exactly what you measure — and fake it.
Predictable Algorithms
When source code is public, attackers know which browser parameters you collect — and which ones you don't. Dedicated browser extensions exploit this to generate fake fingerprints in milliseconds.
Open source = open attack surfaceLow Accuracy (~60%)
Free libraries collect 15–25 browser parameters. Anti-detect browsers (Multilogin, GoLogin, Dolphin Anty) spoof all of them. Mobile devices share most parameters across millions of users.
60–70% vs our 99.5%No Server Validation
Client-side only — the attacker controls the execution environment. The result can be intercepted and replaced before it reaches your server. You're trusting a bot to tell you it's not a bot.
Client-side = untrustworthyARCHITECTURE
Four Layers That Make It Unbreakable
Closed, Dynamic Code
Unlike open-source libraries, Tracio's agent code is generated dynamically. The signals collected, the hashing algorithms, and the obfuscation patterns rotate regularly — making reverse-engineering practically useless. What an attacker learns today is obsolete tomorrow.
Code rotates every few hours1,200+ Device Signals
Canvas fingerprint, WebGL renderer, audio subsystem, CPU cores, memory pressure, font metrics, timezone anomalies, screen orientation physics — plus dozens of proprietary signals we don't document publicly. The combination produces an identifier stable across incognito, cookie clearing, and browser updates.
99.5% identification accuracyServer-Side Validation
Every identification request is cryptographically signed and validated on our servers. The client-side agent cannot be replaced or spoofed — cross-checks detect internal inconsistencies that no attacker can fake without access to Tracio's private keys.
Zero client trust modelGlobal Reputation Network
Tracio processes identifications across thousands of sites. When a fraudster is flagged on one platform, that intelligence propagates network-wide. New bot patterns, anti-detect browser updates, and proxy networks are detected collectively — not in isolation.
Cross-site threat intelligenceBEHAVIORAL ANALYSIS
The Layer Bots Can't Fake
A fingerprint can be spoofed. A cookie can be deleted. But the way a human hand moves a mouse — governed by neuromuscular physics — cannot be replicated by any script or algorithm.
- ›Movement velocity & variance
- ›Direction changes per 100px
- ›Neuromuscular jitter (±1–3px)
- ›Trajectory curvature (Fitts' Law)
- ›Acceleration & deceleration profile
- ›Key hold duration (dwell time)
- ›Inter-key flight time
- ›Bigram timing matrix
- ›Key rollover (overlap)
- ›Error-correction patterns
- ›Scroll velocity variance
- ›Direction change frequency
- ›Reading pause correlation
- ›Scroll momentum physics
- ›Up/down ratio analysis
API RESPONSE
Every Response in Detail
One API call returns everything: a stable visitor ID, a bot score from 0–100, a three-state verdict, and a confidence rating. Use as much or as little as your use case requires.
visitorIdStable across incognito, cookie clearing, and browser updates. Survives VPN switches and IP changes.
bot_score0–100 numeric score. Use thresholds per action: allow at <20, challenge at 20–60, block above 60.
verdictSimplified 3-state output: human · suspicious · bot. Use directly in business logic without threshold tuning.
confidence0.0–1.0 certainty. Low confidence = request more signals or defer to manual review.
See It in Action
Start free and integrate in under 5 minutes. Or book a demo and we'll walk through your specific use case with real data from your traffic.
No credit card · SOC 2 Type II · GDPR ready