PRIVATE AI INFRASTRUCTURE, YOUR PERIMETER

Your own model.
Running inside your walls.

Train models on your own data and serve them at sub-50ms, on hardware you control. Private cloud, on-prem, or fully air-gapped. Not an API in the middle, nothing to meter. Your weights, your servers, your advantage.

See how it deploys →
  • AIR-GAPPED CAPABLE
  • ON-PREM OR PRIVATE CLOUD
  • YOU OWN THE WEIGHTS
  • NOTHING METERED
Numerata delivered as one installable unit A single boxed unit with three internal bays (P95 for training, NinetyFive for deployment, and Lupine for compute), shipped to your own environment. The model it produces stays with you. SHIP TO · YOUR ENVIRONMENT TRAIN P95 your data DEPLOY NinetyFive <50ms COMPUTE Lupine GPUs

One unit: cloud, on-prem, or fully air-gapped. The model it builds never leaves it.

THE WHOLE PRODUCT, IN ONE UNIT

A model in a box. And the model is yours.

Not a server that rents you someone else's model. One unit that trains models on your data, serves them at sub-50ms, and hands you the weights. It installs in your environment and phones home to no one.

It builds models, not just runs one

P95 turns your data into your models. An inference appliance can only serve what someone else already trained.

P95 · Train →

It serves fast enough to sit in the critical path

NinetyFive runs your fine-tuned models at sub-50ms, inside the same box, with no API hop in the middle.

NinetyFive · Deploy →

It brings its own compute, or uses yours

Lupine schedules the GPUs in the box, or pools the ones you already own, and scales to zero when idle.

Lupine · Compute →

You keep what it makes

The weights it produces live on your hardware. Nothing metered, nothing leaves. Walk away and you still have your model.

<50ms

INFERENCE, FAST ENOUGH FOR THE CRITICAL PATH

0

BYTES LEAVE YOUR NETWORK

3

DEPLOY MODES: CLOUD, ON-PREM, AIR-GAPPED

1

FLAT YEARLY PRICE, NOTHING METERED

TRAIN AND SERVE THE OPEN MODELS YOUR TEAM ALREADY TRUSTS

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THE DIFFERENCE

Everyone else ships a safer chatbot.
We ship you a model that's yours.

The difference between renting access to a shared model and owning one trained on your own data, point by point.

Cloud AI API Numerata
Where your data goes Leaves your network Never leaves your perimeter
Who owns the model The vendor You, weights and all
Trained on your domain Shared, general model Your data, your models
Latency Network round-trip + queue Sub-50ms, in your DC
Cost model Per-token, unpredictable One flat yearly price
Runs air-gapped Requires internet Fully offline capable

WHAT TEAMS BUILD ON IT

Built for the work where privacy isn't optional.

One stack, adapted to the job in front of it. Every model trained and served inside your own compliance boundary.

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RESEARCH & ANALYSIS

Train models on the research and proprietary data your team has accumulated, without exposing any of it to a third-party API.

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DOCUMENT & COMMS CLASSIFICATION

Read, route, and classify contracts, filings, tickets, and correspondence on infrastructure that stays inside your own boundary.

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INTERNAL COPILOTS

A coding and knowledge assistant trained on your own repositories and documentation, with no source code leaving the network.

A shield representing risk and fraud detection

RISK & FRAUD DETECTION

Score, flag, and triage against models tuned on your own history, on hardware that never hands that history to anyone else.

Two speech bubbles representing customer-facing assistants

CUSTOMER-FACING ASSISTANTS

Support and client-facing assistants that answer from your own material, with customer data staying on your own servers.

A clipboard with checked items representing compliance and audit

COMPLIANCE & AUDIT

Review, classify, and retain records against an auditable model you control, running where your regulators expect it to run.

RUN IT YOURSELF

Three ways to run it. All of them yours.

Numerata installs inside your environment and your team runs it. Pick the tier your compliance boundary already allows: the same software in all three cases, and what changes is only how much of the outside world your deployment is allowed to see.

WHERE IT RUNS

PRIVATE CLOUD

Runs in a cloud account you own, inside your own VPC. The fastest path to a working deployment, and the most common starting point.

  • Your AWS, GCP, or Azure account
  • Your VPC, your subnets, your key management
  • Egress restricted to your own allowlist
  • Scales to zero between jobs

ON-PREMISES

Runs on hardware you already own, in your own datacenter or colo. Suits teams with existing GPU capacity and a hard preference for keeping it busy.

  • Your GPUs, your racks
  • Scattered hosts aggregated into one pool
  • Integrates with your existing scheduler
  • No cloud account required at any point

FULLY AIR-GAPPED

No network path to us or to anyone else. Installed from signed media, updated from offline bundles, and verifiable by your own security team before anything runs.

  • Zero outbound connections, by construction
  • Offline install and update bundles
  • Nothing phones home, ever
  • Full dependency manifest for review

OUR PHILOSOPHY

01 OWNERSHIP

Your model, trained on your code,
in your environment.

02 PRECISION

Milliseconds matter. So does being right.
A private model gets you both.

03 COMPOUNDING

Every commit, every fix
compounds into a model only you own.

Your best work shouldn't live on someone else's servers.

The stack installs in your environment (private cloud, on-prem, or fully air-gapped) and stays there.