Watch the engine protect a real children's learning product — live.
Instant Tutor is a live K-12 learning product by Educational Tutorial Services. Every AI turn in its student and parent portals runs through the Veridect engine — a deterministic child-safety screen in front, multi-model consensus behind it, and an output screen before anything reaches a child. The bench below fires real attack patterns through the same four production stages the live portals run, in the same order with the same configuration — and shows you every verdict as it happens.
What you're looking at
Our team wrote the scenarios below, and the bench runs them against the live production integration of the Veridect engine — real integration, real enforcement, shown in the open. So call it what it is: a demonstration, honestly run, not independent validation. That track exists too — a credentialed Fortune 100 model-risk reviewer has already exercised the engine adversarially, the validation record is public, and companies are invited to run their own attacks against a private instance under agreement.
Every run below is live — no scripted results, no staged answers. Attacks stopped by the deterministic screen never touch an AI provider, so most of the protection you'll watch costs nothing to run. One benign control question goes all the way through the engine, because a screen that blocks real homework would be worse than useless. All personas are synthetic; no real student data is ever involved.
Self-harm signals are never met with a cold block: the student sees a supportive reply pointing to a trusted adult and the 988 lifeline, and the escalation is recorded in the turn's decision record.
One turn, followed all the way down.
The Governance Suite tour follows one enterprise decision through the entire control plane. This page is the same idea at its sternest: one child's message, four stages, nothing taken on faith.
Policy in plain code, before any model is called
Deterministic detector families for the patterns that target children: grooming-shaped lures, personal-information solicitation, off-platform contact and secrecy pressure, explicit-content requests, dangerous-activity instructions, roleplay jailbreaks, self-harm signals. No model judgment, no prompt cleverness — pattern families written and reviewed as code, so the floor of protection never depends on an AI having a good day.
Two details risk teams notice. A student who volunteers their own address or phone number is escalated too — the system protects a child who overshares, not just one being hunted. And the screen never logs a child's words: the audit record carries the category, the pattern class, a hash, and a length — never the text.
What a block costs: zero provider calls, milliseconds. Fails closed.
The screening every production turn gets
Content moderation and prompt-injection screening — the table-stakes layer much of the market sells as the whole product. Here it is one stage of four.
The deep check, spent where it belongs
Attacks never get this far — they die at the screens, in code, for free. A clean question is what earns the expensive scrutiny: the benign control goes to four frontier models from independent vendors, answering in parallel and checked against each other, and comes back as one consensus answer with a calibrated confidence read. One vendor's blind spot is hard to hide from three competitors. We don't spend that on every turn — the deep checking goes where the stakes are — and runs like this one carry a trace id you can quote back to us.
The answer itself is screened before a child sees it
Even a clean question can draw an unsafe answer. The output screen checks what the engine produced — contact details, off-platform nudges, secrecy language — and if it fires, the child sees a safe reply instead and the intervention is recorded, stage-attributed, in the turn's decision record.
Runs are rate-limited — if you hit the limit, give it a minute. Scenario text lives server-side; this page only ever sends a scenario id.
What a green bench proves
- The Veridect engine is integrated on a real production path in a live, regulated-context product — not a slide.
- Child-safety harms are stopped before any AI provider is called, deterministically, with the decision on display.
- Benign schoolwork flows through untouched — protection without over-blocking.
- Every decision carries its trace: stage-by-stage verdicts, engine consensus, and a downloadable decision record.
And what it doesn't
- It is not third-party validation — first-party scenarios, written by our team, and we say so.
- It is not a guarantee against every attack ever conceived; it is the production stack's own stages, demonstrated honestly.
- Fixed bench by design: outside organizations author their own attacks in a private instance under agreement — that evidence stays theirs to publish on their terms.
The same discipline, grown up.
Everything this bench demonstrates is a production capability of the Veridect platform, proven in education and deployed in production under Fortune 100 contracts in regulated industries — sized here for a children's product, sized there for an enterprise AI estate. Where each one leads:
Verify. Govern. Prove. The bench you just ran is all three, in miniature.
Want to run your own attacks?
Children are the least forgiving users an AI system can face. The discipline on display here — deterministic policy before any model is called, screening on the way out, a decision record for every turn — is the same discipline an enterprise AI estate needs.
Bring your security or AI-risk team, author your own scenarios, and test a private instance you control — with pre-agreed publication terms.
Talk to the CEO