Services · Last updated Nov 2026

Private AI deployment that keeps your data inside your walls.

Most firms with sensitive documents stall at the same question: where does our data go? We answer it before the build starts. The model runs inside your own cloud account, or on hardware you control, and your documents are never sent to us or stored by us.

Talk through the build

The system

What we build

Data boundary design, agreed in writing before any build

Claude deployed in your own AWS or Google Cloud account

Open-weight models on your own hardware when nothing may touch an external API

Access control, audit logging, and retention set to your policy

One production workflow built on the private stack, then handed over

Built for the work

Why it matters

Every private deployment starts with a written data boundary: which systems the model can read, where it runs, who can see the logs, and what never leaves your network. Then we build one workflow on it in about six weeks and hand your team the repository.

Your documents stay in your tenancy
A named workflow live in about six weeks
Your team owns the code, the prompts, and the logs

How the work comes together

Why private AI deployment comes first for regulated firms

Funds, lenders, banks, and family offices hold documents they cannot paste into a consumer chat app: credit agreements, data rooms, LP records, ledgers. Until someone can say exactly where that data goes, every AI project stops at the compliance review.

A private deployment removes that blocker. The model sits inside the same boundary as the rest of your systems, under the same access rules and the same audit trail. The question changes from whether AI is allowed to which workflow to run first.

How the work comes together

The three ways to deploy a private model

Your own cloud account. Claude is available through Amazon Bedrock and Google Cloud Vertex AI. It runs under your cloud identity, your network rules, and your logging. This is the default for most firms, because you keep frontier quality and reuse controls your security team already approved.

A business plan with admin controls. Claude Team and Enterprise plans keep your workspace separate and exclude your data from training. For drafting, reading, and analysis by a small team, this is often enough, and it needs no infrastructure.

Your own hardware. An open-weight model runs on servers you own, with no external API at all. It suits air-gapped or strict data-residency cases. The tradeoff is real: open models are behind frontier models on hard reasoning, so we scope the workload to what they do well.

How the work comes together

What we agree before the build

We write a one-page data boundary with your security or compliance lead. It names the systems the model can read, the region it runs in, who can see prompts and logs, how long they are kept, and what is never sent anywhere. Nothing is built until that page is signed off.

Then we build one workflow on the private stack, with every extracted figure linked to its source page and a check that blocks anything that does not reconcile. At handover you get the repository, the infrastructure setup, and a working session with your team.

How the work comes together

When private deployment is the wrong answer

If your data is not confidential, a private stack adds cost without adding value, and we will say so. If your team only needs help drafting emails, a business plan is enough. Private deployment earns its place when the documents are sensitive and the workflow is worth automating.

A few useful details

Questions about private ai deployment

What is private AI deployment?

Private AI deployment means the language model runs inside an environment you control, such as your own cloud account or your own servers, instead of a shared consumer app. Your prompts, documents, and outputs stay under your access controls and your logging.

Which deployment option is right for us?

For most firms it is Claude inside your own AWS or Google Cloud account, because you get a frontier model with your existing cloud controls. A team plan with admin controls can be enough for lighter use. Open-weight models on your own hardware fit the rare case where nothing may touch an external API, at a lower quality ceiling.

Is our data used to train the model?

Under the published terms of the major cloud model services and Anthropic's business plans, your inputs are not used to train models. We still write the boundary down, and your counsel should confirm the current terms before go-live.

Do you see or keep our data?

No. We build inside your environment with access you grant and can revoke. Your documents are not copied to our systems, and the access is removed at handover.

How long does a private AI deployment take?

The private stack itself is usually the short part. Most of the six weeks goes into the first workflow running on it, so the deployment pays for itself instead of sitting idle.

Open tools

Try it free before you hire us

These free builds from our AI build library show exactly how we approach private ai deployment. Run one yourself this weekend — when you want it wired into your real stack, that is the engagement.

Business operations