Prynt generates a stable, tamper-proof device identifier by combining 100+ browser, hardware, and network signals — persistent across sessions, incognito mode, VPN switches, and cookie clearing.
Cookies get cleared. IPs change. Basic fingerprinting is easy to spoof. You need something fundamentally better.
Cleared by users, blocked by browsers, wiped in incognito. Safari ITP limits cookies to 7 days. A visitor who clears cookies becomes a stranger.
Shared by NAT, rotated by ISPs, masked by VPNs. Millions of users can share one IP. One user can show dozens of IPs in a single day.
Hardware-rooted, server-verified, ML-enhanced. Survives everything — incognito, VPN, cookie clearing, browser upgrades. One device, one ID, forever.
Prynt collects signals across six independent layers. Each layer produces a partial fingerprint. Combined, they generate a high-entropy identifier that's nearly impossible to spoof.
Prynt's fingerprint persists where cookies, IP, and basic fingerprinting fail. Here's what it survives — and how.
Hardware signals (Canvas, WebGL, Audio, fonts) are identical between normal and incognito sessions. Our fuzzy-matching links the incognito visitor back to their original ID instantly.
Prynt doesn't depend on cookies, localStorage, or IndexedDB. The fingerprint is generated from hardware and rendering characteristics that can't be "cleared" by the user.
Device fingerprints are based on hardware and rendering — not network attributes. Changing VPN servers, proxies, or even ISPs does not affect the generated visitor ID.
When a browser or OS updates, some signals change incrementally. Prynt's fuzzy-matching algorithm accounts for gradual drift and correlates the updated fingerprint to the existing visitor ID.
We continuously measure identification accuracy across billions of real-world requests. Here's how Prynt compares to alternative identification methods.
Client-side-only libraries hit a ceiling around 60% accuracy. Prynt adds server-side ML models that correlate signals across sessions, enabling fuzzy matching when individual attributes change.
Every fingerprint is matched against the visitor's signal history. Gradual changes (browser update, font install) are tracked and absorbed, maintaining identity continuity across months.
Six independent signal layers means no single point of failure. Even if one layer is blocked or spoofed, the remaining layers maintain identification accuracy above 95%.
Our models are retrained weekly against real-world data. New browser versions, OS updates, and anti-fingerprinting techniques are detected and incorporated automatically.
Native SDKs for every platform — same visitor ID format, same API, same signals. Identify users across devices and platforms with a unified interface.
Lightweight JavaScript agent. Runs in under 4ms. Supports all modern browsers including Chrome, Firefox, Safari, and Edge.
Native Swift SDK. Uses hardware identifiers, sensor data, and device configuration for stable identification — persistent across app reinstalls.
Native Kotlin SDK. Leverages hardware specs, cellular data, battery state, and sensor configuration for deep device identification.
Every identification request returns the visitor ID, confidence score, device metadata, and session history. Access via Server API, sealed results, or webhooks.
pv_{alphanumeric}. Remains consistent across sessions, incognito, VPN, and cookie clearing. Lasts months.true for returning visitors, false for first-time devices.{
"requestId": "req_1707832921_a7f2c9",
"visitorId": "pv_8kX2mNqR3jT7p",
"visitorFound": true,
"confidence": 0.995,
"firstSeenAt": "2025-09-14T08:12:33Z",
"lastSeenAt": "2026-02-13T14:22:01Z",
"device": {
"platform": "macOS",
"platformVersion": "14.3",
"browser": "Chrome",
"browserVersion": "121.0.6167.85",
"hardware": {
"type": "desktop",
"gpu": "Apple M3 Pro",
"cores": 12,
"memory": 16,
"screen": {
"width": 3024,
"height": 1964,
"dpr": 2,
"colorDepth": 30
},
"touchSupport": false
}
},
"hashes": {
"canvas": "d4e8f1a2c6...",
"webgl": "b9c3d7e0f5...",
"audio": "a7f2c9e1b3...",
"fonts": "e3a1b4c7d2..."
},
"meta": {
"totalSessions": 347,
"incognitoSessions": 28,
"browserUpgrades": 4,
"fontsDetected": 287
}
} Device fingerprinting is the foundation for every fraud, security, and personalization workflow in Prynt.
Recognize trusted devices instantly. Flag new or suspicious devices at login and trigger step-up authentication only when risk is real.
Link registration attempts to existing devices. Catch serial account creators using the same hardware — even across incognito sessions.
Associate payment attempts with device history. Flag card testing, velocity anomalies, and new devices making high-value transactions.
Prevent users from redeeming promotions repeatedly by detecting the same device across new accounts, incognito sessions, and VPNs.
Recognize returning visitors without requiring login. Restore cart contents, personalize content, and streamline checkout for known devices.
Count real unique visitors instead of cookie-inflated numbers. Get accurate session counts, return rates, and conversion attribution.
Free trial. Unlimited API calls. No credit card.