Service
Private AI Deployment
For teams with the highest security requirements: a private LLM deployed inside your environment, so sensitive data never leaves your perimeter.
A private deployment means your models, data, and infrastructure stay in environments you control — your cloud tenant, your VPC, or fully on-premise. Nothing leaves your perimeter, and no third party trains on your data.
It is the cleaner path for regulated and mission-critical work, because the protected data simply never travels to a vendor in the first place. You also get flat-rate pricing instead of unpredictable per-token billing.
What you get
-
Inside your perimeter
Deployed in your cloud tenant, VPC, or on-premise. Your data and the model that touches it stay where your controls already apply.
-
Auditable by design
Every decision the AI influences records its inputs, output, source documents, and model version in a durable, queryable log.
-
Flat-rate pricing
Predictable cost instead of metered per-token billing that punishes adoption.
Built for compliance
We design against the regulations you already operate under — including HIPAA, SOC 2, GLBA, and ISO 27001 — plus any client-specific requirements. Every engagement begins with a data map, and access is scoped, logged, and auditable end to end.
Auditability designed in from day one is far stronger than auditability bolted on after a pilot, so we treat the audit trail as a first-class part of the system rather than an afterthought.
When a private deployment is the right call
If protected health information, privileged material, financial records, or citizen data would otherwise have to leave your environment to use AI, a private deployment removes that exposure entirely.
It is the deployment model we recommend whenever the cost of a data-handling mistake is measured in regulators, lawsuits, or lost trust rather than dollars.
Frequently asked questions
- What does a private AI deployment actually mean?
- Your models, data, and infrastructure stay in environments you control: your cloud tenant, your VPC, or fully on-premise. Nothing leaves your perimeter, no third party trains on your data, and access is scoped, logged, and auditable end to end.
- Is ChatGPT HIPAA compliant?
- The consumer version of ChatGPT is not HIPAA compliant and should not be used with protected health information. HIPAA compliance depends on the deployment, not the model — and a private deployment inside infrastructure you control is the cleaner path because protected data never leaves your perimeter.
- Can AI decisions be audited?
- Yes, if the system is designed for it from the start. Every decision the AI influences should record its inputs, its output, the source documents it used, and the model version that produced them, all in a durable, queryable log.
Putting private, context-aware AI to work in a regulated environment? We should talk.
Book a demo