Outcomes you get
- Reproducible infrastructure defined as code
- Predictable spend with budgets, tagging and rightsizing
- Security and compliance controls evidenced, not assumed
Solution
Cloud & Platform Engineering
Multi-cloud AI platforms on AWS, Azure and Google Cloud with CI/CD, IaC and cost control.
Conceptual visualization only
The problem we solve
AI workloads land on infrastructure that was never designed for them, so cost, security and reliability all drift.
What we deliver
- Target architecture and migration plan
- Infrastructure-as-code modules and environments
- CI/CD pipelines with automated checks
- Observability stack: logs, metrics, traces, alerts
- Cost report, backup and disaster-recovery procedures
A good fit if
- Teams moving pilots into production hosting
- Organisations consolidating across multiple clouds
- Workloads with cost overruns or missing controls
How This AI Agent Works
The published process for this agent: what triggers it, what it validates, which approved tools it calls, where a human approves and what it delivers.
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.
72%
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.
Capabilities
Delivered as consulting, build, automation or managed service.
Multi-cloud architecture
Cloud migration
Serverless AI
Docker and Kubernetes
CI/CD pipelines
Infrastructure as code
Cost optimization
Backup and disaster recovery
Observability
Security and compliance
How an engagement runs
Phase 1 · 1 week
Discover
Current-state review, constraints and compliance needs.
Phase 2 · 1–2 weeks
Design
Target architecture, network and identity model.
Phase 3 · 3–8 weeks
Build
IaC, pipelines, migration waves, observability.
Phase 4 · Ongoing
Deploy & improve
Cutover, cost tuning, DR testing, handover.
Common questions
Do you have a preferred cloud?
No. We deliver on AWS, Azure and Google Cloud and choose based on your existing estate and skills.
Can you work with our platform team?
Yes. Co-delivery with your engineers is the default so operations stay in-house after handover.
Ready to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.