Unified AI access
A consistent entry point for applications, tools and agentic workflows across different supported model providers.
Aegis is an early-stage platform initiative designed to help organizations adopt multiple AI models while controlling access, tenant separation, risk, spend and operations.
Status: In developmentThese are product goals and intended capabilities, not claims that a hosted commercial service is already generally available.
A consistent entry point for applications, tools and agentic workflows across different supported model providers.
Centralized governance with authentication, request authorization and sensitive-data handling before model dispatch.
Namespace, RBAC, network policy and secret isolation principles designed into the platform from the start.
Tenant-aware usage measurement, budget enforcement and model routing decisions informed by cost.
Planned reliability controls, backup and recovery procedures, monitoring and well-defined fail-closed behavior.
Cloud-native Kubernetes workloads and GitOps configuration patterns to support more than one cloud environment.
We plan to use AWS to evaluate core infrastructure capabilities and reduce the operational risk of early enterprise AI deployment.
These are candidate services under evaluation; use of any service depends on technical and cost validation.
Infrastructure-as-code, workload identity, network boundaries and secret management.
Functional integration, tenant isolation tests and cost-aware AI routing.
Observability, backup recovery, failure simulations and multi-cloud operations.
Technical conversations with potential design partners and developers.