AI safety API detecting grooming, bullying, scams, self-harm and CSAM across text, voice, image and video. 1.1-1.5s typical response, zero content retention.

Tuteliq detects grooming, bullying, self-harm and exploitation in real time. This helps the platforms kids use every day actually protect them, not just log what happened after.

1.1–1.5s response. Zero content retention. EU data residency. KOSA, COPPA, DSA and GDPR Art. 8 aligned.

Trusted by 343+ companies worldwide

Safety (bullying, unsafe, grooming, distress signals) · Safety Extended (coercive control, vulnerability exploitation, gambling harm, radicalization, TFGBV) · Fraud (social engineering, APP fraud, romance scam, mule recruitment)

Text, voice, image, video & document analysis

Languages our detection models support, with native cultural calibration (separate from the 27 EU member states used for jurisdiction rules)

KOSA, COPPA, UK OSA, EU DSA, GDPR & CAADCA

Real-time detection at scale

Zero-retention architecture by design

“Every child deserves to be just a child. We built the technology to make that possible.”

COMPLIANCE & MEMBERSHIP

Backed by Real Compliance

Independent standards for data protection and child safety

GDPR Compliant

Built to meet EU data protection requirements

EU-Based

Headquartered in the European Union

Zero Content Retention

Submitted content is deleted immediately after inference. Never stored, never used for training.

COPPA Aligned

Supports compliance with the Children's Online Privacy Protection Act

Proud IWF Member

The Internet Watch Foundation is the UK's leading organisation dedicated to the removal of child sexual abuse material from the internet.

Everything You Need for Child Safety

Complete toolkit built by safety experts, designed for developers.

Grooming & Coercion Detection

Trained on real exploitation patterns with child psychologists. Catches trust-building, isolation, and boundary-testing, not just keywords.

Fraud & Scam Detection

Social engineering, romance scams, mule recruitment and app detection, catch financial exploitation targeting young people.

Synthetic Content & Deepfakes

Detect AI-generated text, deepfake images, cloned voices, and manipulated video. Forensic analysis across four modalities, with automatic critical escalation for synthetic CSAM.

Age & Identity Verification

Verify user age through document analysis and biometric estimation. Liveness detection prevents impersonation. Confirm who's a child and who's an adult, before detection starts.

Multimodal Analysis

Text, voice, image, and video analyzed through a single API. Audio transcribed with timestamped safety analysis. Images classified visually and OCR-scanned for embedded text. Real-time voice streaming for live monitoring.

3 Lines to Production

npm install, initialize, analyze. No ML expertise needed. Ship compliant in under 5 minutes.

How It Works

Get up and running in under 5 minutes with three simple steps. Add age verification with one additional line.

“A groomer never says what a filter expects. They say what a child needs to hear. That is what we detect.”

Install SDK

Add our SDK to your project with a single npm, Swift Package Manager, or Gradle command.

Configure & Analyze

Initialize with your API key and start analyzing content in real-time.

Monitor & Report

Get real-time alerts on critical incidents. Generate audit-ready compliance reports with one click.

Your engineers ship the API. Your safeguarding team works here.

Every Tuteliq plan includes a full Trust & Safety console. No extra licence, no separate login, no third-party tool to integrate. Real-time monitoring, queue management, and incident review out of the box.

End-to-end encrypted by default

Every incident is encrypted at rest with AES-256-GCM. Moderators must explicitly decrypt to view, every access is logged.

Designed for moderator wellbeing

Sensitive content blurred until requested, "Take a break" always one click away, exposure tracked per shift.

Risk monitor INDUSTRY SAMPLE

Sample view based on external industry signals, not Tuteliq's own network. See the Live Impact section for Tuteliq platform telemetry.

Detection breakdown

Regional activity

Keyword filters miss what groomers actually do.

No single message in this conversation contains a banned word. A naive moderation filter sees nothing. Tuteliq reads the conversation the way a trained safeguarding specialist would, tracking isolation patterns, trust escalation, and age-asymmetric flattery across turns.

  • Multi-turn behavioural analysis (not just message-level)
  • Age-aware risk thresholds: a 13-year-old gets different protection than 16
  • 32-language cultural calibration, not literal translation
  • 1.1-1.5s typical latency, deployed at the edge

"Rapid trust-building, isolation from the parent, and flattery creating a sense of special connection. The secrecy request is a strong grooming indicator. The child is 13, peak vulnerability age."

What single-message moderation cannot see.

General-purpose classifiers score one message at a time, and they are excellent first-pass filters for explicit content. The harm patterns that matter most to child safety only exist across a conversation.

Representative case

Single-message classifier

Tuteliq

Multi-turn grooming across a long conversation

Missed, no trajectory across turns

critical, eight grooming tactics scored

Investment scam built over weeks

No fraud category

critical, social-engineering endpoint

Romance-scam mirroring tactic

Reads as ordinary affection

critical, romance-scam endpoint

Coercive control between partners

No category

critical, isolation and control signals

AI-assisted impersonation of a peer

No synthetic-content signal

critical, synthetic content detection

Sarcastic bullying ("just delete your account")

Often missed, no explicit slur

high, exclusion pattern flagged

Gaming trash talk between friends

Often a false positive

not flagged, context aware

Self-harm disclosure seeking help

Detected

detected, plus localised support routing

Cases are representative of patterns our detection endpoints are built for. Outcomes vary with conversation length, language and context.

Child safety infrastructure, not generic content moderation

CSAM hash-matching and keyword filters miss the threats that matter most. Tuteliq detects the behavioral patterns of grooming, coercion, and emotional manipulation, the blind spots other tools ignore.

Grooming is a behaviour, not a word

Groomers do not use flagged words. Tuteliq reads the trajectory, trust-building, isolation, escalation, so you catch the pattern rather than the vocabulary.

The cost of not detecting

Regulators have issued penalties in the hundreds of millions for the same failure: platforms unable to detect harmful content at scale. Detection is now the cheaper option.

Purpose-built, not bolted on

A fine-tuned model trained on expert-labelled child-safety data, plus a patent-pending Multi-Signal Forensic Pipeline (PRV 2630405-5). General-purpose LLMs are a tiebreaker, not the core detector.

“The technology to protect children online did not exist. So we built it.”

Verify Before You Protect

Built-in age verification and identity verification with liveness detection. Confirm who's actually a child and who's actually a coach, before behavioral detection even starts. No third-party vendor required.

Behavioral Detection, Not Keywords

Built on criminological research into how exploitation actually unfolds. We detect grooming patterns, not just banned words, 0.945 recall and 0.940 precision in internal validation.

Composable Safety Primitives

Tuteliq isn't a set of detectors, it's a platform. Compose detectors with cross-endpoint risk modifiers and four-dimensional vulnerability profiling to compute compound risk and route interventions by who the target is, not just what was said.

Eight Grooming Tactics

Eight per-message tactics: flattery, secrecy request, isolation, boundary pushing, photo request, gift giving, meeting request, and reconnaissance. Sexual content is a content category, not a grooming tactic.

Built for Global Platforms

Detection at near-English parity across major European languages, German, French, Spanish, Portuguese, Italian, Dutch, Polish, and more, with localized response guides for moderators. Honest about what's stable and what's beta, no overclaiming.

Zero content retention

Content is analyzed in-memory and discarded. No logs, no training on your data, no breach surface. Privacy by architecture, not policy.

One SDK, Six Regulations

KOSA, COPPA, CAADCA, UK Online Safety Act, EU DSA, and GDPR Art. 8 coverage from a single integration. No stitching together multiple vendors.

1.1-1.5s typical latency

Deployed on a global edge network, not a centralized API behind a queue. Typical end-to-end response 1.1-1.5s on shared infrastructure, 99.99% uptime SLA.

Explore the Platform

Dive deeper into our technology, team, and mission.

Every child deserves to explore the internet without fear.

302 million children are victims of online sexual exploitation every year, 10 children every second.

Predators don't use keywords, they use psychology. And the technology that should protect children is being weaponized against them.

302M

children are victims of online sexual exploitation every year

Childlight / University of Edinburgh, 2024

10

children harmed every single second

Childlight, 2024

546K

grooming reports in 2024, up 192% from the year before

NCMEC, 2025

1,325%

increase in AI-generated child abuse material in one year

Every second we wait, another child is harmed.

“We do not sell safety. We build infrastructure so that safety is the default, not the exception.”

"The conversation starts with 'What games do you play?' and ends somewhere no child should ever be. The gap between those two moments is where Tuteliq works, detecting the patterns of manipulation before harm occurs."

Dr Nicola Harding

Chief Scientific Officer, Tuteliq

Whether you're a developer building the next social platform, a company with a duty of care, or simply someone who believes children deserve better, you can be part of the solution.

“Cancelling child safety protection is not a technical decision. It is a public one.”

Stop Building Moderation. Start Shipping.

1,000 free API calls per month. Full KOSA coverage from day one. Age verification included. Go from npm install to production in under 5 minutes.

14-day free trial · Build from $99/mo · Enterprise custom pricing

14-day free trial · No card charged for 14 days · Cancel anytime

Child Safety API for Developers

Tuteliq is the child safety API built for developers who need to protect young users in real time. Our AI models — trained with child psychologists on real exploitation patterns — detect grooming, cyberbullying, self-harm, romance scams, mule recruitment, radicalisation, coercive control, gambling harm, social engineering, and vulnerability exploitation across text, voice, image, and video.

Ship KOSA readiness plus COPPA, EU Digital Services Act, and UK Online Safety Act compliance in under 5 minutes with official SDKs for Node.js, Python, Swift, Kotlin, Flutter, Unity, React Native, and .NET.

How Tuteliq Works

Integrate child safety in three steps: install the SDK, configure your detection thresholds, and call the analyse endpoint. Every API response includes a risk score, confidence level, detected patterns, and recommended actions — giving your trust and safety team everything they need to act.

Trusted by Developers Building Safer Platforms

Tuteliq powers child safety for gaming platforms, social networks, messaging apps, educational tools, and metaverse environments. Our API processes millions of interactions daily with 99.9% uptime and enterprise-grade security.

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