When you put an AI in front of users, you inherit responsibility for what it says. A jailbreak, a hallucinated promise, an off-brand answer — any of these can land on a customer and on your liability. Output guardrails are how you keep your AI on the rails.
The prompt isn't the only risk — the response is
Most teams think about filtering user input. But the dangerous moment is the response: the model generating explicit content, unsafe instructions, a competitor comparison, or a refund promise you can't honor. Screening only the input misses this entirely. You need to screen what the model says before it reaches the user.
What output guardrails check
Emil screens every response across 14 safety categories — violence, self-harm, hate, sexual content and more — using a deterministic explicit backstop plus a model classifier. It also catches PII and secrets leaking into the output, and enforces brand rules you define in plain language: 'never give medical advice,' 'never mention competitors.' Anything that violates the policy is blocked or redacted before the response is returned.
Drop-in, one line
You don't rebuild your stack. Point your OpenAI-compatible client's base URL at Emil and every request and response is screened — no SDK, no middleware. It works with Claude, GPT, Gemini, and self-hosted models alike.
Prove it works
Claiming your AI is safe isn't enough for enterprise buyers or auditors. Emil ships a red-team evaluation harness that runs a battery of adversarial prompts through your policy and reports the catch rate by category plus a false-positive rate. That's the evidence that turns 'we have guardrails' into a number you can stand behind.