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
“300 million children. One API call. Detection that reads the pattern, not the word.”
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.
From verification to compliance. One pipeline, four pillars.
Click each step to explore what Tuteliq does at every stage of the safety lifecycle.
Know who your users are
Document-based or liveness-only verification with jurisdiction-granular overrides across 27 EU member states and 15 US states.
Age Verification
Selfie-only or document-based. ICAO MRZ, PDF417, multi-pass OCR, liveness detection.
Identity Verification
128-dim face matching, 7-layer fraud detection, ban evasion prevention, risk-based step-up.
Jurisdiction-granular overrides
Per-EU-state (27 countries, consent ages 13–16) and per-US-state (CAADCA, SCOPE Act, and more).
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.
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.
What Our Partners Say
Trusted by teams building safer products
"We don't want any more children to slip through the gaps and Tuteliq can help make that a reality."
Kerry Smith
CEO
"At SEEN we deliver personalised video at scale for some of the biggest brands in retail and financial services."
Ronald Griffin
"Instead of reacting to issues after they surface, we now have better visibility into potential risks before they escalate."
Ron Novak
CEO and Co-Founder
"The fast-evolving safeguarding environment led us to a dedicated tool."
Hugh Shepherd
Founder & CEO
"Tuteliq helps us keep our service away from underage users without requiring intrusive identity verification."
Eva Leach
"Tuteliq's AI-driven risk assessments let me identify vulnerabilities early, saving weeks of manual audits."
Gaurav Vashisth
Partner
"As an AI developer, one of my biggest concerns has always been how users interact with our agents, especially when young users are involved."
Dev Rishi Khare
Founder
FOR TRUST & SAFETY
Protecting Children Worldwide
Real-time signals from Tuteliq's own protection network, live since January 2026.
65K+
Children Protected
163K
Threats Blocked
249K
Wellbeing Checks
799
Active Families
Detection Breakdown
Regional Activity
“Cancelling child safety protection is not a technical decision. It is a public one.”
Give Your Team a Safer Queue by Monday.
One unified moderator console. Grooming, bullying, self-harm, sextortion and CSAM detection across text, voice, image and video, with reviewer reasoning and a public Trust Center.
Reviewer console · Audit-ready · 32 languages