Preemptive
De-escalation.
Toxicity is an emotional state before it is a behavior. nx10 empowers Trust & Safety teams to detect severe frustration and intervene before a harmful message is ever sent.
The Blind Spot
The post-harm reaction.
Whether you are running a secure messaging platform, a dating app, or a massive multiplayer game, traditional Trust & Safety infrastructure is fundamentally reactionary.
Invasive NLP Filtering
Current toxicity filters rely on Natural Language Processing (NLP). This requires scanning and reading a user's private messages. Not only is this a massive privacy liability, but it only flags the abuse after the message has been sent.
By the time your algorithm catches the slur, the victim has already been harassed, and your moderation team has another manual ticket in their queue.
The Nx10 Solution
Toxicity is rarely a cold, calculated act; it is the result of peaking emotional arousal and severe frustration. nx10 monitors this build-up silently using 100% content-blind kinematics.
The Large Feelings Model (LFM) analyzes typing cadence, micro-tremors, and swipe pressure to detect "Rage Typing". You can intercept the user, calm them down, and prevent the message from ever leaving their device.
The T&S Ecosystem
Protect the community.
Empower the moderators.
Head of Trust & Safety
Goal: Reduce platform toxicity incidents globally.
Shift your KPIs from "Messages Blocked" to "Incidents Prevented". By deploying emotional circuit breakers, you drastically reduce the volume of abusive behavior occurring on your platform, creating a safer environment that drives user acquisition.
Legal & Privacy
Goal: Maintain strict E2EE and GDPR compliance.
Because the nx10 SDK processes kinematics (physics) and not semantic text, you can maintain end-to-end encryption (E2EE) and absolute message privacy while still actively moderating extreme behavior.
Moderation Teams
Goal: Reduce manual review backlog and PTSD.
Human moderators suffer extreme burnout reviewing toxic content. By stopping the content before it's sent, you drastically lower the volume of manual review tickets, protecting your workforce's mental health.
The Playbooks
From science to systems.
How leading communication and dating platforms are utilizing nx10 to build safer, more authentic communities.
Preemptive Toxicity Prediction
A user is losing an argument in a game's text chat, or is rejected on a dating app. They begin furiously mashing their keyboard to type out a string of abuse.
The Implementation:
By monitoring the Game Behaviour Index (GBI) via the SDK, the client app detects the "Rage Typing" signature (extreme physical arousal, rapid backspacing). Before they can hit 'Send', the app temporarily disables the send button and slides up a calming UI prompt: "Take a breath. Messages sent in anger are usually regretted." You diffuse the situation instantly.
func startMonitoringChat() {
Nx10.observeGBI { state in
if state.category == .severeFrustration {
// User is rage-typing. De-escalate.
self.sendButton.isEnabled = false
showDeescalationPrompt() {
// Re-enable after 10s cool-down
self.sendButton.isEnabled = true
}
Analytics.log("Toxicity_Prevented")
}
}
}
app.post('/webhooks', (req, res) => {
if (req.body.eventType === 'kinematic_anomaly') {
const accountId = req.body.sourceId;
// Signature matches automated/farm behavior
trustService.flagAccount(
accountId,
RiskLevel.CRITICAL
);
// Require human verification immediately
authService.forceSelfieVerification(accountId);
}
});
Romance Scams & Bot Farms
Romance scammers and click-farms use residential proxies and device spoofing to perfectly mimic a legitimate user's digital footprint. But they cannot spoof human emotion.
The Implementation:
A scammer running 50 dating app accounts exhibits a cold, detached, perfectly flat kinematic baseline completely devoid of the micro-arousals expected during genuine romantic or social chat. The LFM flags this kinematic_anomaly via Webhook, allowing you to instantly isolate and shadow-ban the farm.
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