Guardrails so your AI never says what it shouldn't
Emil screens the model's response — not just the prompt — across 14 safety categories plus rules you define, blocking or redacting before the output reaches your user.
A jailbroken model generates explicit, harmful, or hateful content that lands on your users.
Your AI invents promises — refunds, legal or medical claims — you can't honor.
Off-brand or competitor-mentioning answers damage trust.
What Emil screens output for
Explicit, harmful, and unsafe content (14 safety categories)
PII and secrets leaking into the response
Off-brand claims and unauthorized promises (custom rules)
A deterministic explicit backstop plus a model classifier
Prove it works
Ships with a red-team evaluation that scores catch rate by category
Reports a false-positive rate on benign content
Define 'never say' rules in plain language — Emil enforces them
Questions
Can Emil stop my AI generating explicit content?
Yes. Emil screens the model's output, with a deterministic explicit backstop plus a model classifier across 14 safety categories, and blocks or redacts before the response is returned.
How do I define what my AI must never say?
Beyond the built-in policies, you write natural-language rules — 'never give medical advice', 'never compare us to competitors' — and Emil enforces them on the output.
How do I integrate output guardrails?
Point your OpenAI-compatible client's base URL at Emil. Every response is screened automatically — one line, no SDK.
Can I show evidence to buyers and auditors?
Yes. Emil ships a red-team eval harness that scores your policy's recall across unsafe categories and reports false positives — the proof enterprise buyers ask for.