Anthale is a runtime trust layer for LLM applications. It gives you one place to evaluate risky content and one set of runtime actions to enforce before that content reaches a model, a user, or a downstream system.
Where Anthale sits
Anthale does not replace your application. It sits beside it. Your application still owns authentication, product behavior, and user experience. Anthale owns policy evaluation. In practice, that means you call Anthale before a model request, before risky retrieved context enters the prompt on the input path, or before model output is released.
What problem it solves
Without a dedicated enforcement layer, most teams end up spreading safety logic across prompts, provider settings, helper functions, and emergency patches. That approach becomes hard to reason about as soon as the application grows. Anthale replaces that drift with policies, guardrails, and a clear runtime result: allow, detect, redact, or block.
When Anthale is a good fit
Anthale fits best when your team needs one runtime contract across several trust boundaries. Typical examples include user input before model invocation, retrieved context before prompt assembly, tool output before reuse, and model output before it reaches a user or downstream system.
What Anthale is not
Anthale is not your identity system, your business authorization layer, or your entire security program. It also is not a prompt-only patch for jailbreaks. It is one focused layer in the runtime path of an AI application.
Who usually uses it
You will usually see Anthale in customer support assistants, RAG systems, internal copilots, and agent workflows that combine user input, retrieved context, and tool output. The common thread is not the interface. The common thread is that risky content can move through the system quickly, and someone needs to decide what happens before it does.
Next steps
Read How Anthale Works for the request lifecycle, or start the Quick Start Overview if you want to learn by doing. Last modified on March 19, 2026
