Private AI Cloud on AWS, Azure & Google Cloud
Your own AI environment, inside your own cloud account
We design, deploy and run private AI environments in your AWS, Azure or Google Cloud account: isolated networking, your encryption keys, the models you approve, a private AI workspace for staff and separate knowledge bases for each department — with nothing exposed to the public internet.
AI that stays inside your boundary
Many organizations want their staff to use AI with real company information — contracts, case files, financials, personnel records — but can’t send that information to a public AI service. Their security and compliance rules require it to stay inside infrastructure they control.
Your cloud account is already that boundary: it holds your data, your identity setup and your security policies. We build a private AI environment inside it, so AI comes to your data rather than the other way round.
Built for your security team
Every component — models, search indexes, storage and logs — is reached through private endpoints, encrypted with keys you hold, and deployed in the regions you choose. Access goes through your identity provider, and every request is logged to your own storage.
The environment is defined as code, so your security team can review exactly what is deployed, and the same design can be repeated for test, production or another business unit.
A typical private AI environment
Your people & apps
- Company AI workspace
- Teams & intranet
- Internal apps & agents
AI gateway
- Sign-in & rolesYour identity provider
- Policy & redactionBefore any model call
- Limits & auditUsage, cost, logs
Models
- Managed modelsBedrock, Azure OpenAI or Vertex AI
- Open modelsOn your own GPU instances
Knowledge bases
- Company-wide
- HR, Legal, FinanceEach kept separate
- Per client or project
- No public access
- Customer-managed keys
- Your regions
- Logs in your storage
- Infrastructure as code
What’s in the environment
Isolated network
Models, search and storage reached only through private endpoints inside your network — no public access to any component.
Your encryption keys
Data, indexes and logs encrypted with customer-managed keys in AWS KMS, Azure Key Vault or Cloud KMS.
Your region
Everything deployed in the regions you choose, so data residency rules are met by design.
Approved models only
A catalog of the models your organization has approved — managed or self-hosted — and nothing else.
AI gateway
One entry point for every app and user: single sign-on, roles, redaction, usage limits and cost tracking.
Company AI workspace
A private chat workspace for staff, signed in with your identity provider, with access to the knowledge bases each person may use.
Audit & retention
Every prompt, answer and action logged to your own storage, with retention rules you set.
Infrastructure as code
The whole environment defined in Terraform, Bicep or AWS CDK — reviewable, repeatable and easy to hand over.
Separate knowledge bases, one platform
Not everything should be searchable by everyone. Each department, client or project can have its own knowledge base — its own index, access group and, where needed, its own encryption key — while sharing the same gateway, models and operations.
Company-wide content such as policies and procedures sits in a shared collection; restricted content stays with the people allowed to see it. Each knowledge base is built the same way as our Company Knowledge Base & RAG service: permission-aware, kept current and cited.
Managed or self-hosted models
Managed models
Claude, GPT, Gemini, Llama and Mistral models through your cloud provider’s AI service, in your account and region, under terms that exclude training on your data. Pay per use, with no servers to run.
Self-hosted open models
Open-weight models served with vLLM on GPU instances in your own network. Nothing leaves your environment, and cost is fixed by the hardware rather than by usage.
Most environments use both behind the same gateway: managed models for general work, self-hosted models for the most sensitive data or high-volume jobs.
On the cloud you already use
We build on your existing cloud account and landing zone, using each provider’s native private networking, key management and audit services.
Amazon Web Services
- Amazon Bedrock through VPC endpoints (PrivateLink)
- Bedrock Knowledge Bases or OpenSearch
- GPU instances or SageMaker for open models
- AWS KMS, IAM Identity Center & CloudTrail
Microsoft Azure
- Azure OpenAI through private endpoints
- Azure AI Search on a private network
- GPU virtual machines or AKS for open models
- Key Vault, Entra ID & Conditional Access
Google Cloud
- Vertex AI & Gemini inside VPC Service Controls
- Vertex AI Search or AlloyDB with pgvector
- GKE GPU node pools for open models
- Cloud KMS, Cloud IAM & Cloud Audit Logs
How we deliver
Assess
Agree requirements with your security and compliance teams: data types, regions, models and who needs access.
Foundation
Deploy the isolated environment — network, keys, identity, gateway and logging — as code in your account.
Pilot
One team, one or two knowledge bases and the workspace, with usage and quality measured.
Roll out
Add departments, knowledge bases, models and applications on the same foundation.
Operate
Monitoring, model updates, cost reviews and support — by us or your team.
Contact us
Bring AI inside your cloud boundary
Tell us which cloud you run on and what your security rules require, and we’ll outline a private AI environment that meets them.
- 202.747.3355
- [email protected]
- 1717 K St., N.W. Suite 900, Washington, DC 20006