Architecture

Why the tail does not move.

Everything NOL8 claims comes back to three properties.

01

Inline placement

Data passes through NOL8. There is no way around it.

NOL8 works inline rather than adding a path, so nothing makes a round trip out to a general-purpose CPU and back to get read.

Coverage becomes a property of where the layer sits, not a matter of configuration discipline.

02

Deterministic matching

Patterns, not probabilities.

Same input, same result, every time. The control does not depend on a model’s judgment of intent, so there is nothing to sample and nothing to tune.

03

Dedicated hardware in the path


Software-defined, on accelerated hardware.

A breakthrough design based on years of advanced computer science research.

Deployment

NOL8 is available on AWS.

Deploying NOL8 is a placement decision, not an integration project.

You point traffic at the engine and nothing else on your side changes: there is no SDK in your application and no second store to keep in step. Your models, frameworks, orchestration and guardrails run as they do today.

Data stays in the region it is processed in. Payloads are inspected where they pass through, so nothing leaves the region to be checked, and the record of what happened can be kept there too.

Common questions

No. NOL8 replaces one. There is no round trip out to a general-purpose CPU and back.

The tail stays flat as you add patterns, from 1 to 8,000, because NOL8 runs on dedicated hardware in the path. Run it on your own data and watch your own latency at P99 stay flat.

Because the hardware fleet has to grow every time the rule set or data volume does, and latency grows. NOL8 holds throughput as the rule set or data volume grows.

NOL8 is software-defined, accelerated by dedicated hardware in the path, a breakthrough design based on years of advanced computer science research.

Uncertainty sucks.

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