Your models. Your data. Your cloud.

Managed by us. Controlled by you.

Give your teams a ChatGPT grade AI platform that runs inside a cloud environment your organization can inspect, govern, and audit. Every frontier model. None of the black box.

01

No training on your data

02

Customer managed keys

03

We cannot decrypt. Only you can.

04

Pilot in 72 hours

In production at Fortune 500 fintech Fortune 500 energy $10B+ robotics & AV
01 — The problem

You want the AI. You cannot accept the exposure.

Every enterprise has a mandate to adopt generative AI. Far fewer are willing to route source code, contracts, customer data, and strategy through an API they do not operate, on servers they cannot inspect, run by a company that could one day compete with them.

A

Exposure

Where does our data actually go once it leaves the network, and who on the other side can see it?

B

Dependency

What stops our model vendor from becoming our competitor, or simply changing the deal we built on?

C

The black box

We cannot secure, audit, or explain what we cannot inspect. That is not a governance policy.

The real question every enterprise is asking: what are we required to trust, and what can we independently control?

02 — The solution

So we gave you the whole environment.

Your Cloud deploys a complete, managed AI platform inside a cloud environment you can inspect and govern, on AWS, Google Cloud, or another approved environment. We run it. You hold the visibility and the keys.

01

Deployed where you can see it

A dedicated, customer-visible environment with direct console visibility and inspectable network, compute, storage, identity, and logging.

02

You approve the models. We run them.

Self-host frontier open-weight models and swap them as the field moves, without rebuilding your governance, integrations, or workflows.

03

Your teams use it their way

A polished chat and coding workspace, or your own applications and agents connected directly to the platform API.

03 — Trust model

Trust you can define, limit, and verify.

Every managed system requires some trust. The question is which responsibilities you delegate, which controls you retain, and how you verify the boundaries are respected. We map it across six control surfaces.

Infrastructure control

You determine which account and region hosts the system, what compute runs, what is public versus private, and whether resources are dedicated.

Hybrid deployment: a dedicated environment we manage, or inside your own account.

Identity & access

Our administrative access is identity-bound, time-limited, logged, and subject to your policy. You can review who can access the environment, and revoke it.

No invisible administrative access.

Data control

You define what data enters, where it is stored, and how long it is retained. Your data is processed only within the approved environment and is never used to train shared models.

You hold the keys. We cannot decrypt. Only you can.

Model control

Approve models, pin versions, choose self-hosted or externally accessed, and replace a model without disrupting the application layer.

Controlled optionality, not an infinite catalog.

Operational control

We handle deployment, upgrades, monitoring, incident response, patching, backups, and capacity. You retain authority over the policies that govern it.

We operate within the boundaries you define.

Exit control

Export your data and configurations, revoke our access and keys, and know exactly what is portable and what remains after termination.

Control includes the ability to change direction.
Never used to train shared models. Your data is processed only within the approved environment.
Customer managed encryption. You hold the keys. We cannot decrypt your data. Only you can.
04 — Models

Use the model that fits the work. Including ones you could not touch before.

A growing roster of frontier open-weight models behind one consistent enterprise interface. As stronger models ship, you get access inside the same governed platform, with no re-architecture.

01Kimi K3
02GLM 5.2
03Qwen
04DeepSeek
05MiniMax M3
06Nemotron
+New releasesas they ship
The wedge

Because these models run self-hosted inside your boundary with no external inference traffic, you can put frontier open models to work that you would never be permitted to reach over their public APIs.

  • You control it.

    Open weights run inside your boundary, under your policy, on your schedule.

  • You stay current.

    Adopt the best new model the week it lands, without renegotiating your stack.

  • You match model to task.

    Use the best model for coding, analysis, reasoning, documents, or agents.

  • You can verify it.

    Fully transparent, fully self-hosted, fully controlled by you.

05 — Headless

An operating layer, not a walled garden.

Every approved model is available through a consistent API inside your environment, so your teams consume it however they work best. No lock-in to our interface either.

01

Use our workspace

A polished chat and coding experience for employees who want a product that is ready to use on day one.

02

Bring your own harness

Point your existing applications, agent frameworks, and internal tools at the platform API.

# inside your boundary
POST https://api.your-cloud/v1/chat
"model": "glm-5.2"
03

Bring your own coding tools

Connect the agentic coding tools your engineers already use, including Claude Code, to models running inside your boundary.

06 — Alternatives

The control of self-hosting. The convenience of managed.

Without the compromise either usually forces.

“We will just use a frontier vendor's enterprise API.”
Still their servers, their black box, their potential to compete with you. Contractual promises versus visible, verifiable control.
“Azure OpenAI or Bedrock already runs in our cloud.”
A single-vendor model menu, and you still operate it. We give you true model-flexibility, and we run it for you.
“We will self-host open models ourselves.”
Now you own a GPU bill, an ML infrastructure team, and a 24/7 pager. Keep the control without the burden.
07 — For your team

One platform. A line for every seat at the table.

Engineering pulls it in. Security and the CIO bless it. Everyone gets what they need to say yes.

CTO
architecture / velocity / portability

Give engineering access to advanced models through a consistent platform that evolves as the model ecosystem changes.

CISO
boundaries / access / auditability

Define and inspect how models, data, identities, and networks interact, instead of relying solely on a provider's external controls.

CIO
dependency / adoption / flexibility

Establish a durable enterprise AI platform without tying your organization to one model provider.

Get started

See it running in your environment in 72 hours.

We stand up a working pilot instance inside a cloud environment you control, so your engineering and security teams can evaluate it on their own terms.