Cloud
AWS
Design, deploy and operate AI and data workloads on AWS with managed security, observability and cost control.
- • Amazon Bedrock
- • SageMaker
- • Lambda
- • EC2
Cloud
AI platforms engineered for the cloud you already use
We design and operate AI workloads across AWS, Azure and Google Cloud with security, observability and cost discipline.
Conceptual visualization only
Cloud platforms
Cloud
Microsoft Azure
Design, deploy and operate AI and data workloads on Microsoft Azure with managed security, observability and cost control.
- • Azure OpenAI
- • Azure AI Foundry
- • Azure Machine Learning
- • Azure Functions
Cloud
Google Cloud
Design, deploy and operate AI and data workloads on Google Cloud with managed security, observability and cost control.
- • Vertex AI
- • Gemini APIs
- • Cloud Run
- • Cloud Functions
How This AI Agent Works
How the cloud architect agent moves from requirements to an approved, monitored deployment. Nothing is deployed without human approval.
Cloud architecture workflow
From workload requirements to a security-reviewed, approved deployment.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
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Workflow summary as a text list
- 1. Requirements: Trigger. Workload, availability, residency and budget constraints.
- 2. Assess workload: AI agent. Profile traffic, data gravity and compliance needs.
- 3. Select approved cloud services: Decision. Choose only services on the approved catalogue.
- 4. Design architecture: AI agent. Produce the architecture, networking and IaC outline.
- 5. Estimate cost and risk: Approved tool. Model monthly cost and single points of failure.
- 6. Security review: Validation. Check identity, exposure, encryption and logging.
- 7. Human approval: Human approval. A platform owner approves before any deployment. Human approval required.
- 8. Deploy: Action. Apply the approved infrastructure-as-code plan.
- 9. Monitor: Monitoring. Track cost, availability and security signals.
- 10. Architecture and runbook: Output. Diagram, IaC plan, cost model and operations runbook.
Approval gate: Human approval. Deliverable: Report.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| cloud-architecture-workflow | 10 | Yes. Policy: always | Report | Available | Available (signed in) | Available via Studio · requires ai_provider | Verified at 390px |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Platform engineering services
- Multi-cloud architecture
- Cloud migration
- Serverless AI
- Containerization
- Docker and Kubernetes
- CI/CD
- Infrastructure as code
- Cost optimization
- Backup and disaster recovery
- Observability
- Security and compliance
Cloud credentials are never hard-coded. All provider access uses encrypted server-side secrets and environment variables, scoped with least privilege.
Ready to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.