What is a private AI deployment?

It is AI that runs inside infrastructure you control, your own cloud tenant or your own hardware, rather than a public tool that sends your data to a third party. A model router sits between your firm and the models and decides, request by request, what can go to a frontier API and what has to stay local. Sensitive and non-public data never leaves your environment.

Does this mean we cannot use Claude or other frontier models at all?

No. That is the point of the router. You get frontier-model capability where the data is safe to send, and local inference on your own hardware where it is not. You are not forced to choose between capability and control. You get both, with a clear policy deciding which path each request takes.

Do we need to buy GPU hardware?

Sometimes, and sometimes not. Some firms run local models on dedicated GPU hardware on premises. Others keep everything inside their own cloud control plane. We have stood up on-premises proofs of concept on dedicated GPU hardware where the data sensitivity justified it. We scope the right architecture to your data, your budget, and your regulatory obligations before anything gets bought.

How does this hold up with compliance and legal?

We design the governance with compliance and legal from the start, not as an afterthought. The architecture itself is the strongest compliance argument: if regulated data physically cannot reach a public API, most of the hardest questions answer themselves. We pair the build with policy and access controls so it survives a SOC 2 audit or a regulatory examination.

Use AI without your data ever leaving the building.

The answer to “we want to use AI but we cannot put client data into a public tool” is not to ban AI. It is to run it inside your own environment. Frontier capability where it is safe to use it, local inference where the data is sensitive.

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Fixed-fee discovery first. We scope the right architecture before anything gets built or bought.

Zero

sensitive requests sent to public APIs when the router is configured correctly

Two paths

frontier capability where it is safe, local inference where it is not

Your tenant

the model runs inside infrastructure you control, not a vendor black box

The real problem is not whether to use AI. It is where the data goes.

When an employee pastes a client document into a public chatbot, that text leaves your control. For a firm handling regulated, privileged, or non-public information, that single action can breach a client commitment, a protective order, or a regulatory obligation. Banning the tools does not work either, because the work is genuinely faster with them and your team knows it.

Private AI removes the trade-off. The model comes to your data instead of your data going to the model. Sensitive requests are answered inside your environment, and only non-sensitive work uses a public frontier model, by policy, not by hope.

How a private AI deployment works

Four parts, one outcome: capability without exposure.

A model router in front of everything

A router sits between your firm and the models. Every request passes through one place where policy decides where it can go. Nothing reaches a public API by accident.

Local inference for sensitive data

Requests that touch regulated or non-public information are answered by a model running on your own hardware or inside your own cloud control plane. The data never leaves.

Frontier models where it is safe

Non-sensitive work still gets the capability of frontier models like Claude. You are not stuck with a weaker tool for everything because some of your data is sensitive.

Governance built in from the start

Identity, access, managed-browser patterns, and an acceptable-use policy designed with compliance and legal so the deployment holds up under audit and examination.

In practice

An on-premises AI proof of concept for an SEC-registered adviser

For a financial services firm handling non-public client data, we stood up a local large-language-model deployment on dedicated GPU hardware. A model router sends only safe requests to a frontier API and keeps everything sensitive on the local model. The firm gets modern AI capability, and non-public information never leaves their environment.

This is the direct answer to the question every regulated firm is asking right now: how do we use AI without putting client data into a tool we do not control.

Wondering whether private AI fits your firm?

A short call is enough to tell you whether your data sensitivity justifies a private deployment, and roughly what the architecture would look like.

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AI Readiness Checklist

The questions every regulated firm should answer before adopting AI

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The Regulated Firm's AI Readiness Checklist

Six questions that decide whether your firm can adopt AI without putting client data, a renewal, or an examination at risk. Walk them before your next audit, not after.

Where client data is leaving your environment through public AI tools
Whether your AI controls would survive a SOC 2 audit or an examination
Where a human, not the model, needs to ratify the output

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Common questions

AI Advisory overview

The full picture: govern it and build it, for regulated firms.

How to use AI without putting client data in a public tool

The plain-language version of the architecture above.

Bring the model to your data.

Start with a 30-minute call. We will tell you honestly whether private AI is the right answer for your firm and what it would take to stand it up.