An LLM is a powerful generation engine, but it isn't a child-safety classifier. Built-in content filters catch obvious explicit text in a single message; they don't reliably catch grooming arcs, coercion, or synthetic CSAM. Tuteliq sits on the input and output edges to catch what an LLM's native filters miss.
An LLM tells you if a message it's about to send is explicit. Tuteliq tells you a 34-year-old is grooming a 12-year-old across six messages, before anything explicit is said. They're not competing for the same job, and using both is the right architecture: the LLM as the generation engine, Tuteliq as the deep behavioral and KOSA-aligned safety layer on top.
Pick LLMs if
- You need to generate, summarise, translate, or reason over text
- Conversational fluency and knowledge are the core requirement
- You're in an adult-only, low-risk context
- Built-in refusal behavior is sufficient for your policy needs
Pick Tuteliq if
- Minors interact with your LLM-powered product
- You need grooming, coercion, and exploitation detection across many turns
- KOSA, UK OSA, or EU AI Act compliance is in scope
- You need EU data residency, audit trails, and a moderator console
Feature-by-feature
Capability
Tuteliq
LLMs
Text generation & reasoning
Multi-turn grooming detection
Sextortion / romance scam patterns
Synthetic CSAM (deepfake) detection
Output safety screening for minors
Age verification (document + liveness)
Cultural calibration per language
EU data residency (default)
Zero-retention contract
DSA Statement-of-Reasons exporter
Moderator console
Things you'll hear from LLMs
And our straight answer.
Modern LLMs already refuse unsafe content.
Native refusals are tuned for blatant single-message requests and are routinely defeated by jailbreaks and role-play. They don't model a slow grooming arc that never includes an explicit ask. Tuteliq evaluates the behavior of the conversation, independent of the model's own filter.
Why add Tuteliq when the LLM vendor handles safety?
LLM vendors optimise for broad, generic safety, not the specifics of child protection and EU regulation. Tuteliq doesn't replace your LLM for generation; it sits on the edges to catch grooming, coercive control, and synthetic CSAM. Many teams run both.
Doesn't a second model call add cost and latency?
Tuteliq's P95 is 1.1-1.5s typical and runs alongside generation, with async webhooks where latency is critical. A 14-day free trial lets most teams validate the value before it costs anything.
The verdict
Use an LLM to generate and converse. Add Tuteliq as the dedicated child-safety layer on the inputs and outputs, the behavioral and compliance depth LLM filters were never designed to provide.