Ephemeral by default.
Privileged by design.
Run AI over privileged data in isolated, ephemeral compute. Requests execute in protected environments, exit through controlled static egress, and leave no persistent session state behind.
PRODUCT VISUALIZATION · ILLUSTRATIVE VALUES
One infrastructure boundary
instead of an AI supply chain.
Your lawyers shouldn't need to understand your AI supply chain to trust it. Privileged collapses five vendor relationships into one security boundary.
Nothing persists, because persistence is not part of the execution model.
Request → Authenticate → Authorize → Provision → Isolate → Execute → Stream → Audit → Destroy. The full lifecycle of a privileged request, by design.
Receive
An authenticated request enters through the Privileged gateway. Identity, scope, and policy are resolved before any compute is provisioned.
Isolate
An ephemeral environment is provisioned for the workload — single-tenant, read-only root, RAM-only scratch. No persistent disk is attached.
Execute
The model and temporary context exist only for the active execution. Output streams out through controlled, static egress as it is produced.
Destroy
On completion the environment is torn down and memory released. Only payload-free metadata is retained. Teardown is verified, not assumed.
A protected space that appears, does the work, and disappears.
Every privileged request runs inside a boundary that exists only for the length of the work. When the session ends, the environment and everything in it are gone.
Four primitives. One security boundary.
Compute, network, models, and data — each engineered as a product surface, not a feature checkbox.
Compute that exists only when the workload does.
Environments provision on demand, run your model in RAM, and are destroyed when the session closes. No persistent disk is ever attached to a privileged workload.
Private AI without unpredictable network behavior.
Every request enters and exits through controlled, static egress with a fixed infrastructure identity your firewall can whitelist once. Default posture is deny unless explicitly allowed.
Bring the model you actually want to run.
Host private and fine-tuned models in isolated environments, or run open and Privileged-hosted models through one logical identifier. Weights stay yours and are never used for training.
Legal data through one controlled interface.
Reach case law, dockets, regulations, and your own sources through a single provider abstraction — governed by the same policy and audit layer as everything else.
Simple API outside. Sophisticated infrastructure underneath.
One OpenAI-compatible endpoint fronts a control plane, an ephemeral runtime, and controlled egress — each separated by an explicit security boundary.
Security is a property of the architecture, not a marketing promise.
Each control is engineered at the infrastructure layer and enforced in code — not retrofitted through policy language.
Zero-retention applies within the boundaries Privileged controls. Persistent workflows are opt-in and customer-configured. No certification is claimed on this page.
The numbers that actually matter.
Not throughput benchmarks — the architectural facts a security team verifies.
Privileged cloud, at a glance.
Architecture states describe how a privileged workload runs — not live production telemetry.
Built for the people accountable for the data.
Run AI against privileged matter data without creating another uncontrolled data silo.
- Litigation & discovery analysis
- Document review & drafting
- Matter and precedent search
- Internal knowledge retrieval
- Private inference over sensitive files
Infrastructure your lawyers and engineers can agree on.
Privileged sits between technical and legal requirements — so neither team has to compromise to trust the other.
- Static network boundary
- Tenant isolation
- Encryption in transit
- Controlled egress
- Auditability
- Data processing agreement
- Retention controls
- Subprocessor transparency
- Defined security boundary
- Policy enforcement
- OpenAI-compatible API
- Model flexibility
- Custom deployments
- SDKs & CLI
- Observability
Private AI shouldn't require a private cloud.
Run privileged inference without carrying the operational burden of building the entire stack yourself. Tell us what you're trying to run — we'll help determine the appropriate private inference architecture.