Serve

Check the prompt before the model.

Check the response before the user.

Nothing is sampled, in either direction.

Where you are today

Someone is always waiting.

Every prompt and every response crosses the path while a user watches, and there is no batch window to hide the inspection in.

So most stacks check a sample, and usually on one side only. The prompt gets looked at; the response, the half an audit asks about, goes out unread.

What changes with NOL8

Every request, not a sample.

Prompts, context and tenant boundaries are checked before the model sees them, and responses before the user does.

At 8ms P99 at the engine, so the user never waits on the check.

Both crossings, one policy

What goes in and what comes back out are two different exposures, and most stacks only check one of them.

On the way in

The prompt, the context attached to it and the tenant boundary, checked before the model sees any of it.

On the way out

The response, checked before the user sees it. This is the half an audit asks about, and the half most stacks skip.

With NOL8, the same rules apply in both directions, per rule, per agent, per role.

Why the latency tail holds

Turning coverage up does not move the number.

Deterministic matching gives the same result every time and holds throughput flat as the rule set grows, from 1 pattern to 8,000, for over 40 petabytes of data per year per node.

That is the NOL8 breakthrough enabling full byte-level inspection.

8ms

P99 at the engine

8000

patterns, throughput flat

Common questions

NOL8 inspection runs at 8ms P99 at the engine, and the tail stays flat as you add patterns, from 1 to 8,000. Run it on your own data and watch your own P99 latency stay flat.

Yes. NOL8 applies the same rules on the way in and on the way out: prompts and context before the model sees them, responses before the user does.

NOL8 checks context and tenant boundaries before the model sees them, per rule, per agent, per role.

No. A guardrail judges intent and returns a probability; keep it for what needs judgment. NOL8 matches patterns before the prompt reaches the model and again before the response reaches the user, and gives the same result every time.

Uncertainty sucks.

Stop estimating. Your rules, your payloads, your P99.