AI-powered engine harvesting 1,200+ browser signals to compute a stable visitor ID across sessions, incognito mode, and browser updates. 99.5% accuracy, sub-50ms latency.
Browser tampering nearly doubled to 4.4% in 2025. 13% of desktop sessions now run on virtual machines.
Fingerprint Device Intelligence Report, 2026
AI-powered signal harvesting across hundreds of browser, device, and network dimensions for maximum identification accuracy.
Rock-solid identification that survives session boundaries, incognito tabs, cookie wipes, and browser version upgrades.
ML models assign a dynamic confidence weight to each identification based on signal quality, freshness, and uniqueness.
Signals separated into hardware, browser, and volatile tiers — absorbs minor drift while preserving identification accuracy.
AI-driven algorithms reconnect returning visitors even when individual signals have shifted between sessions.
Encrypted first-party cookies backed by localStorage fallback for maximum cross-session persistence.
Our lightweight SDK collects 1,200+ browser signals in under 50ms with zero impact on user experience.
Server-side AI engine analyzes signals, applies advanced matching, and computes confidence scores.
Get a stable visitor ID, bot detection results, smart signals, and IP intelligence in a single API response.
A few lines of code, one API response with everything you need.
import Tracio from '@tracio/client';const tc = await Tracio.load({ apiKey: 'your-api-key' });const result = await tc.get();console.log(result.visitorId); // "X7fh2Hg9Lk..."console.log(result.confidence); // 0.995console.log(result.signals); // 1,200+ signalsOur identification system separates 1,200+ signals into three stability tiers. Tier 1 (hardware signals like canvas, WebGL, and audio fingerprints) forms the core identity and rarely changes. Tier 2 (browser-level signals like feature detection and CSS queries) changes with browser updates but is handled through advanced matching. Tier 3 (volatile signals like user agent and timezone) is used for confidence scoring but never breaks identification. This tiered approach maintains stable visitor IDs across browser updates that break simpler hash-based systems.
When a returning visitor presents slightly changed signals (e.g., after a Chrome update), our server-side engine computes a similarity score using weighted Hamming distance across signal tiers. Hardware-level signals carry 4x the weight of volatile signals. If the composite similarity exceeds the confidence threshold (default 0.85), the same visitor ID is returned. This eliminates the identity fragmentation that plagues static fingerprinting solutions.
Visitor IDs survive incognito sessions, cookie wipes, VPN switches, and even browser changes on the same hardware. Hardware-level signals — canvas rendering, WebGL parameters, audio DSP characteristics — stay constant regardless of privacy settings. Server-side profile matching then reconnects the visitor in scenarios where cookie-only solutions lose track entirely.
Every identification response includes a confidence score between 0 and 1. This score reflects the probability that the current signal set belongs to the identified visitor, accounting for expected signal drift, the number of matching signals, and the uniqueness of matched signals. Scores above 0.95 indicate a near-certain match; scores between 0.85 and 0.95 indicate a probable match with some signal changes; scores below 0.85 trigger a new visitor ID assignment.
See how teams use Device Identification to solve real-world problems.
Recognize returning fraudsters across sessions, VPNs, and incognito mode.
Learn moreFlag login attempts from unrecognized devices before attackers gain access.
Learn moreDetect when one person creates multiple accounts on the same device.
Learn moreGet started in minutes. No third-party data sharing, full data ownership, complete control.
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