AI infrastructure
that pays for itself.
Nol8 processes AI data in flight on dedicated hardware. Your agents stop waiting, your bill stops climbing, and your compliance team stops blocking launches.
The production layer for AI.
Every agent call passes through the data path.
Agents read, write, and call tools constantly. That traffic hits the data path before it ever reaches a model.
Dedicated hardware, not another CPU tier.
Inspection, filtering, and routing run on Nol8 silicon, inline with traffic. No sidecar. No fleet to scale.
One fixed line, not a usage bill.
Data-plane spend stops moving with traffic. Finance plans against hardware, not invoices.
Run the AI workloads that pay the bills.
Inference, agents, retrieval, and regulated workloads on one appliance, one cost line.
Inference at scale
Route, filter, and inspect every request before it reaches the model. Lower compute, cleaner inputs, better outputs.
Agents in production
Persistent agents stop waiting on the data plane. Predictable tail latency at any concurrency.
RAG and retrieval
Match, rank, and route at line rate. Embeddings stay fast, retrieval stays fresh, GPUs stay fed.
Regulated AI
PII redaction, geo policies, full inspection logs. Compliance enforced at the data plane, not bolted on.
Four things change the day you turn it on.
Your agents stop waiting.
P99 stays flat under load, not just at P50.
Your infra bill stops climbing.
Variable CPU spend becomes one fixed appliance line.
Compliance stops blocking launches.
Every interaction inspected and logged at the data plane.
GPUs stop eating junk.
Filter and route before embedding. Lower compute, higher quality.
In the data path. Not next to it.
Nol8 sits inline and does the heavy work in flight, so data reaches your model already clean, fast, and logged.
Silicon in the path.
Dedicated chips that do the work inline at line rate. One rack replaces a CPU fleet. Zero hops added.
- ▸ 3.2M events/s per box
- ▸ sub-millisecond p99
- ▸ 1U appliance, fits any rack
Policy you can read.
The control plane that inspects, filters, and routes every event as it passes. Declarative rules. Full audit trail.
- ▸ declarative policy DSL
- ▸ live inspection logs
- ▸ hot-reload, no restart
Built for production AI.
Every capability points at one outcome: AI workloads that run predictably, cost what they should, and clear compliance on day one.
Teams shipping AI at real scale.
Named studies and logos unlock progressively as legal clearance lands. Metrics below are from production deployments.
Agentic trading copilot, EU region. Replaced a 600-node CPU inference tier with a single Nol8 rack under MiFID II controls.
Regulated inference pipeline for citizen services. PII redaction and audit logging enforced in-line at the data plane.
Variable GPU and egress spend across 14 regions collapsed into a single fixed-cost appliance line on the FY26 budget.
The questions everyone asks.
Architecture, throughput characterization, and policy DSL reference are in the docs. Or talk to us.
Inline, between your clients or agents and your model or storage tier. Traffic flows through the appliance; the appliance does inspection, filtering, redaction, and routing in flight. No sidecar, no extra hop.
No. Nol8 is transport-aware, not model-aware. It speaks the protocols your stack already uses and your model sees clean inputs without any code changes.
Pre-processing on CPUs is general-purpose and serial. Our silicon does the same parsing, matching, and policy work in parallel at line rate. One rack of Ares hardware sustains the throughput a large CPU pre-processing tier handles today.
Every event is inspected and logged at the data plane. PII redaction, geo policies, and access rules are enforced inline by Argus and produce a full audit trail. SOC2 aligned out of the box.
Run a three-week benchmark. We capture a slice of your real traffic, run it through Nol8, and hand back latency, cost, and quality deltas you can take to your CFO.
Ships as a 1U appliance to your colo or as a managed rack in supported regions. Bring-your-own-DC or fully managed — same software, same numbers.
Ship your AI workload
on one rack.
Three-week benchmark on your workload. You'll see it on the bill before you see it in the slides.