Identity & isolation
Designed for verified identities, tenant-specific policy boundaries and least-privilege access throughout the request path.
Introducing Aegis · In development
Enterprise AI.
Enterprise AI.
Governed by design.
Amediomedia is developing Aegis, a secure, multi-tenant AI gateway initiative for teams building with large language models across hybrid and multi-cloud environments.
Early-stage product development. Architecture, integrations and cloud infrastructure are being evaluated and built.
AEGIS / Architecture conceptZERO TRUST
ApplicationsEnterprise APIs
→
AI AgentsAutomation
→
DevelopersEngineering
↓
AEGIS GATEWAY
DESIGNIdentity · Policy · DLP · Budgets · Observability
↓
ClaudeModels
OpenAIModels
Cloud AIProviders
Conceptual diagram — not a live system status indicator
Security-first architecture
Zero Trust identity and default-deny principles
Multi-tenant by design
Tenant-aware access, governance and resource control
Cloud-flexible foundation
Kubernetes, GitOps and provider-aware integration
The challenge
Powerful models deserve a dependable control plane.
As AI usage spreads across applications, engineering teams need a coherent way to enforce access, protect data, keep spending predictable and maintain operational visibility.
Centralized governance
Bring AI policy, sensitive-data controls, budgets and model permissions into one auditable governance layer.
Provider-aware routing
Enable model selection and upstream fallback without losing the visibility and controls needed by platform teams.
Operational observability
Plan for model usage telemetry, traceability, health monitoring and incident-ready infrastructure workflows.
FinOps & budgets
Make usage measurable and plan tenant-aware budgets, quota controls and model cost optimization.
Repeatable delivery
Use Kubernetes and declarative GitOps practices to make multi-environment deployments consistent and reviewable.
Technical direction
A clear architecture from edge to model.
Aegis is being designed to protect the complete AI request lifecycle instead of treating model APIs as an unmanaged endpoint.
Secure accessCloudflare Zero Trust, Access, Tunnel and Envoy Gateway.
AI governanceLiteLLM for centralized policy; OmniRoute behind the governance boundary.
Infrastructure automationKubernetes, Helm, Argo CD and externalized secrets.
Technology references describe the intended architecture. Specific deployments and capabilities may change during development.
Proposed request journey
01
Identity-aware edge
Verify external access and route requests through trusted ingress.
02
AI gateway routing
Manage HTTP and AI requests through shared, isolated gateway infrastructure.
03
Policy and usage controls
Evaluate tenant permissions, content protection and spending policies.
04
Internal provider dispatch
Apply provider quotas and routing logic behind the governance layer.
05
Telemetry and operations
Measure activity, budget impact, reliability and recovery readiness.
Cloud infrastructure
How AWS fits our development plan →
Exploring AWS for secure, scalable AI infrastructure.
We are evaluating AWS services as part of our cloud-native development roadmap, including infrastructure, security and recovery capabilities.
Amazon EKSAmazon S3AWS IAMAWS KMSAWS Secrets ManagerAmazon CloudWatch
AWS use cases under evaluation Planned
01
Container orchestration
Amazon EKS for repeatable platform environments and AI gateway components.
02
Identity and secrets
IAM, KMS and Secrets Manager for authorization, encryption and credentials lifecycle.
03
Data and continuity
S3 for backup and artifacts; CloudWatch for operational telemetry.