Seven trained, on-device models watch for safety risks in AI conversations, from self-harm to bullying to jailbreaks. They run in a deliberate three-layer pattern so they are both accurate and cheap, and a student in crisis sees help immediately, free, in Basic.
Tenet ships seven trained machine-learning classifiers that run in the browser: jailbreak, self-harm, bullying, illicit content, violence, sexual content, and student-record or peer-PII. They look at the meaning of what a student writes, not just whether it contains a banned word, so they catch real risk while leaving normal academic work alone. Each is opt-in per district with tunable sensitivity.
This pattern is why the safety layer is both trustworthy and fast enough to run on a Chromebook.
A lightweight pre-filter decides whether a prompt is even worth a closer look. About 99 percent of prompts skip the model entirely. This keeps the whole system fast and inexpensive, and is the reason it can run locally on every message.
Anything the trigger flags goes to the on-device model, which returns a confidence-scored decision. Each model is tiny, on the order of tens of kilobytes, and runs with no network call, so prompts stay on the device.
For Pro districts, an on-device language model reads the actual conversation against the teacher's rules. This catches nuanced situations that a single-prompt classifier cannot, like an AI slowly being talked into doing the student's work.
| Classifier | What it detects |
|---|---|
| Self-harm | Suicidal ideation, self-injury, crisis signals, with academic-context guards so literature analysis is not flagged. |
| Bullying | Harassment and cyberbullying language directed at peers. |
| Jailbreak | Attempts to trick the AI past its own guardrails. |
| Illicit content | Requests related to drugs, weapons, and other illegal activity. |
| Violence | Threats and violent intent. |
| Sexual content | Sexually explicit requests inappropriate for a school setting. |
| Student-record / peer-PII | A student sharing IEP, 504, discipline, or other sensitive records, or sensitive information about a peer. |
When self-harm is detected, the student immediately sees an in-browser crisis-resource overlay with 988, the Crisis Text Line, and a local counselor. This is free. The principle is simple: a student in crisis should see crisis resources whether or not the district has paid for the alert layer.
Tenet looks at two signals: whether the student's own message raises concern, and whether the AI's reply surfaces crisis language such as a hotline number. When both fire, that is the highest-confidence incident, and the response is supportive by design, not punitive.
The most common Pro trigger is a real crisis incident. Once a district sees the value of the free overlay, the natural next question is “can the counselor be notified automatically?”, and that is Pro.
Early detection of crisis signals, with supportive intervention rather than punishment, and in Pro an alert routed to the right counselor in seconds.
A defensible, measurable student-safety posture for the board, with interventions you can point to.
It all runs on-device with tunable sensitivity, so there is no new data pipeline and no surveillance of normal student work.
Clear escalation triggers when an incident repeats across classrooms, so the building can respond consistently.