# Run AI within the boundaries your data needs.

Private AI deployment

Place inference and retrieval within agreed data boundaries and document every external provider involved.

## Know where each request goes

Illustrative workflow.

- Private sources: Authorised documents and application data
- Inference boundary: Selected hosting and model endpoint
- Outbound controls: Approved services and redacted telemetry



## A private endpoint is only one boundary

### A private endpoint as the whole plan

Logs, embedding services, backups and support access can create additional processing paths.

### A documented processing boundary

Review all dependencies, identity paths, retention settings and recovery arrangements alongside model hosting.

## Private is an architecture decision

Map the movement of prompts, files, generated output and diagnostic logs. Hosting choice alone does not define a private system when supporting services can still receive its data.

Illustrative scenario, not a customer case study.

A business is considering where sensitive internal inference should run.

Map prompts, embeddings, telemetry, backups and provider support access before choosing infrastructure. Record each processing location and the contractual controls for external services.

Verification: Trace a representative request through every processing and storage destination, including error reporting.

## Control the complete processing path.

### Processing boundaries

Choose where inference runs and which services can receive prompts, files and generated output.

### Identity and connectivity

Configure private access paths, service identities and narrowly scoped credentials for the workload.

### Model operations

Plan model updates, capacity, telemetry and rollback as part of the application’s operating model.

### Inference routing

Select approved model endpoints by task, sensitivity and capacity, with explicit fallback behaviour.

### Data handling

Define prompt and response retention, operational logging and access to captured traces.

### Execution isolation

Separate generated code or untrusted processing from application credentials and production records.

## What your team receives

Included scope agreed before delivery.

- Data-flow map: Prompts, files, embeddings, outputs, logs and every receiving service.
- Deployment configuration: Hosting, identity, network and outbound controls in reviewable form.
- Operating guide: Access review, model updates, monitoring and recovery responsibilities.

## Does private deployment always mean self-hosting?

No. The choice depends on processing terms, network controls, operating capacity and the required models. Compare managed and self-hosted options against the same data-flow requirements.