Bring your own vendors — or let us bring the signals.
unilinx.ai turns scattered fraud and threat signals into one intelligence graph and one decision your team can defend. There are two ways to feed it. If you already run device-intelligence, behavioral-biometrics, network, telco, remote-access and malware vendors, we sit on top of them and correlate what you already pay for. If you don't — or your coverage has gaps — we bring the device, network and behavioral-biometrics signals to you directly, powered by betterbehave.ai. Either way, the graph, the correlation and the explainable verdict are exactly the same.
However the signals reach us, the answer comes out the same.
Most teams start with one of these and grow into both. unilinx.ai doesn't care where a signal originates — it cares how signals relate to each other.
Sit on top of the stack you already run.
You've already invested in device intelligence, behavioral biometrics, network and telco enrichment, remote-access detection and EDR. The problem isn't more data — it's that each vendor sees only its own slice and none of them see each other. unilinx.ai ingests the feeds you already receive, normalizes them, and correlates them into a single live graph. No new SDK. No duplicate collection. No rip-and-replace.
- Connect existing vendor feeds in minutes — nothing new to install.
- We never re-collect data you already own; we make it talk to itself.
- Keep every contract you have — unilinx adds the layer above them.
No coverage yet? We bring the signals to you.
Don't have a behavioral-biometrics vendor? Missing solid device and network intelligence? You don't need to go shopping for six tools first. With a single passive script — no SDK for your users, no friction — betterbehave.ai captures device, network and behavioral-biometrics signals from the first visit and streams them straight into the unilinx graph. You go from zero coverage to a correlated, explainable decision in one integration.
- One line of integration — passive, continuous, invisible to 99%+ of users.
- Device, network and behavioral-biometrics signals from day one.
- Feeds the same unilinx graph any other vendor would.
Both paths converge on the same place: one intelligence graph, one risk decision, and every contributing signal explained.
Three families of signal. One continuous verdict.
When we source signals for you through betterbehave.ai, this is what lands in the graph — captured passively, scored continuously, and correlated across every session and identity unilinx has ever seen.
Behavioral biometrics
How a person types, moves, scrolls and hesitates is as individual as a signature — and far harder to steal. A private, adaptive baseline is built for every user within minutes, then the full session is watched for the moment the rhythm changes. Stolen credentials open the front door; behavior is what gives the impostor away once they're inside. It's the layer attackers can't convincingly mimic, and it never asks the real user to do anything.
- Keystroke dynamics — dwell and flight times, typing cadence and error patterns unique to each person.
- Pointer rhythm — mouse acceleration, curvature and micro-corrections that separate a human hand from a script.
- Scroll cadence — speed, momentum and reading pauses that reveal how a real user consumes a page.
- Gesture & touch physics — pressure, swipe arcs and device tilt on mobile.
- Gaze & attention — where focus lands and how it moves through a flow.
- Dwell & hesitation — the pauses, second-guesses and micro-motion a genuine user produces and a bot doesn't.
Device intelligence
Every device leaves a fingerprint. More than 200 signals — operating system, installed fonts, codecs, GPU, sensors, browser entropy and network path — are read to recognize a returning device and to unmask one pretending to be something it isn't. Headless browsers, emulators, spoofed user-agents and automation frameworks give themselves away in the details they can't fake.
- Persistent device fingerprint across 200+ attributes — stable for the real device, brittle for the fake.
- Headless & automation detection — Puppeteer, Playwright, Selenium and synthetic cursors caught on the first move.
- Emulator & virtual-machine detection — sandboxes and device farms separated from real hardware.
- Spoofing & tamper detection — mismatched user-agents, patched APIs and inconsistent entropy.
- Hardware & environment hints — GPU, sensors, codecs and OS-level tells.
Network intelligence
The network layer is where automated abuse hides in plain sight — a botnet behind residential proxies looks just like your customers until you inspect the path. How a session connects and how fast an identity moves through it are classified, flagging the transport patterns that don't add up.
- VPN, proxy and TOR classification — know when a session is deliberately obscuring its origin.
- Datacenter & hosting-ASN detection — traffic that claims to be a phone but originates from a server farm.
- Impossible-travel checks — the same identity in two places no human could reach in time.
- Velocity & path analysis — bursts, fan-out and routes that signal coordinated abuse.
Three engines behind every sourced signal.
When unilinx sources signals for you, betterbehave.ai does the capture and the first-pass scoring — passively, continuously, with nothing for your users to install. Three engines turn raw interaction into a session-level verdict that unilinx then correlates against everything else it knows.
Behavioral engine
Builds a per-user baseline from typing cadence, pointer dynamics, gaze, dwell and micro-motion — and flags the instant a session stops behaving like its owner.
Device & environment engine
Deep fingerprinting across 200+ signals — OS, fonts, codecs, GPU, sensors, network path and automation hints — to recognize the real and unmask the fake.
Risk orchestrator
Fuses behavior, device and network into one live score with rules, ML and explainability built in — recomputed on every interaction, not just at login.
The attacks that walk straight past static authentication.
Once a session is open, a password tells you nothing. These are the patterns continuous signals catch that front-door checks miss.
Account takeover
Stolen credentials and hijacked sessions clear every up-front check — then behave like someone else entirely.
Bots & automation
Headless browsers and scripts are indistinguishable from real users at the network layer — until behavior and device tells give them away.
Remote access & malware
RATs and overlay malware pilot the victim's own device, sailing past every classic, login-time defense.
From one passive script to a correlated decision.
Sourced signals follow four stages — and only the last one is unique to unilinx.
Capture
A single drop-in script collects anonymized device, network and behavioral signals from the first visit. No SDK for your users, no friction.
Baseline
Each user builds a private, adaptive behavioral fingerprint within minutes — the reference every later interaction is measured against.
Score
Risk is recomputed on every interaction, not captured once at login — a live session score that rises the moment something turns hostile.
Correlate in unilinx
The sourced score and its underlying signals join the unilinx graph, linked to the devices, IPs, users and behavior clusters of every other session — and resolved into one explainable verdict.
Measured in millions of sessions.
Sourced-signal performance as reported by betterbehave.ai across banks, fintechs and regulated enterprises.
See every signal converge on one decision.
Walk through the live demo to watch device, behavioral and network signals correlate into a single verdict — or talk to us about sourcing the signals you're missing.