Use cases
One plane, three phases.
The same layer filters a corpus, checks a prompt and governs a tool call. The rest of your stack stays as it is.
The placement never changes
One plane sits between every source and its destination: a corpus and a training run, a user and a model, an agent and a tool. Same layer, same rules.
Train
Filter the corpus before it is trained on. Opt-out and rights lists, exact dedup, eval-set decontamination and known-bad strings.
Removes the multi-day batch on a large CPU fleet.
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Serve
Check prompts, context and tenant boundaries before the model sees them. Check responses before the user does.
Removes the latency in the tail, and the sampling that avoided it.
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Act
Govern every tool call, retrieval, agent-to-agent payload and output. All of it crosses the plane.
Removes the blind spot over what your agents sent last night.
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Where it fits in the agent lifecycle
NOL8 covers four of the seven agent lifecycle stages: data and context engineering, deploy and gate, operate and observe, scale and optimize.
For partners
Sell certainty as a tier.
Providing global data fabrics, or running inference for distributed workloads? Not for enterprise AI workloads, unless they first trust that only clean data is traversing distributed infrastructure. Partner with NOL8 and unlock enterprise AI workloads to monetize on your infrastructure.
Common questions
Uncertainty sucks.
Stop estimating. Your rules, your payloads, your P99.