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Deployment is designed around your required data perimeter, then validated across inference, telemetry, and support paths.
Enterprise AI depends on the complete deployment model, not only the model. For regulated workloads, we map PII, PHI, and other controlled-data flows before build, contract the required boundaries, and verify them end to end. Managed, BYO-cloud, and air-gapped designs carry different operating assumptions.
We operate the stack inside your AWS, Azure, or GCP account. You own the data plane, we own the uptime.
Where contracted, the platform runs inside your VPC with restricted egress, and audit logs route to your SIEM. The final design is validated against the agreed boundary.
For suitable defense, healthcare, and sovereign-cloud workloads, an air-gapped design can run models, orchestration, and storage without outbound connectivity, subject to technical validation.
Customer data is never used to train shared models, full stop. Fine-tuning stays inside your tenant.
Encryption keys live in your KMS. Rotation, revocation, and access policies are yours to set.
Pin workloads to specific regions to satisfy GDPR, data localization laws, and customer contracts.
Run frontier models via your own accounts, self-hosted open-weights, or sovereign providers — one control plane.