solutions / llm-apis
LLM API protection
Your engineers chose their model months ago. The question is no longer whether production data reaches it — it is whether you can say what reached it.
The problem
Prompts are the least governed egress path in most companies. They are written by hand, contain whatever context made the answer better, and go to a provider whose retention terms change. Blocking the tools does not work: it moves the traffic to personal accounts where you cannot see it at all.
What Divelai does
Point your client at the Divelai proxy and every prompt is classified and sanitized on the way out, including streamed responses. Consistent tokens mean the model still reasons about the customer as one entity across a conversation. Completions are rehydrated before your code sees them, so your product behaves exactly as it did.
What this does not solve
A sanitized prompt is still a prompt. Divelai does not stop a user asking the model to do something unwise, and it is not a prompt-injection defence — it governs the data in the request, not the instruction.
# Streaming works the same way. stream = client.chat.completions.create( model="claude-opus-5", messages=messages, stream=True, ) # Chunks arrive rehydrated, token by token. for chunk in stream: print(chunk.choices[0].delta.content, end="")