Identify every device. Across every browser.
GPU-level fingerprinting that creates a persistent device ID from 42+ hardware signals. Works across Chrome, Firefox, Safari, and Brave — even in incognito mode.
Hardware Signals
Average Latency
Device Matching
Persistence
Capabilities
Everything you need
GPU Fingerprinting
WebGL rendering pipeline analysis creates hardware-specific signatures that uniquely identify GPU models and driver configurations.
Canvas Fingerprinting
Sub-pixel rendering differences in HTML5 Canvas create unique signatures based on graphics hardware, OS, and font rendering engines.
AudioContext Analysis
Audio processing characteristics of the device's sound hardware produce consistent, cross-browser identifiers.
Screen Geometry
Display resolution, pixel ratio, color depth, and available screen dimensions combine into a stable device signature.
Cross-Browser Consistency
The same device ID is generated whether the user visits from Chrome, Firefox, Safari, or Brave. Our core differentiator.
Incognito Persistence
Hardware-level signals don't change between normal and private browsing sessions, making our fingerprints persistent across modes.
Anti-Spoofing
Detect and flag attempts to spoof user agents, screen sizes, or other browser properties. Know when someone is lying about their device.
Sub-40ms Latency
Signal collection, hashing, and server-side matching complete in under 40 milliseconds. Zero impact on page load times.
42+ Hardware Signals
GPU renderer, WebGL extensions, canvas rendering, audio processing, fonts, screen properties, CPU cores, and device memory — all combined.
How It Works
Three steps to get started
Install the SDK
Add the LightningResearch SDK to your project with a single npm install. TypeScript types included.
Call identify()
The SDK silently collects 42+ hardware signals from the visitor's device — GPU, canvas, audio, screen, fonts, and more.
Get the device ID
Receive a stable device fingerprint with confidence score, risk assessment, and metadata in under 40ms.
Integration
A few lines of code
Get LR Trace running in your application with our TypeScript SDK. Full type safety, comprehensive documentation, and framework-agnostic design.
import { LightningResearch } from '@lightningresearch/sdk'
const client = LightningResearch.init({
apiKey: process.env.LR_KEY
})
const result = await client.identify()
console.log(result)
// {
// device_id: "fp_9k2xR...mR4",
// confidence: 0.97,
// signals: 42,
// latency_ms: 37,
// risk: { score: 0.08, level: "low" },
// meta: {
// browser: "Chrome 121",
// os: "macOS 14.3",
// gpu: "Apple M2 Pro"
// }
// }
Use Cases
Built for real-world security
Fraud Prevention
Detect multi-accounting and payment fraud by identifying devices that create multiple accounts or use stolen credentials.
Account Security
Flag suspicious logins from unknown devices. Trigger step-up authentication like MFA when the device fingerprint doesn't match.
Return Abuse Detection
Identify serial return abusers even when they create new accounts, use different browsers, or clear cookies between sessions.
Promotion Abuse
Prevent users from claiming free trials, referral bonuses, or discount codes multiple times by linking their devices.
Get started with LR Trace
Free tier includes 10,000 identifications per month. No credit card required.