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Capability
Cloud Engineering
We land products and AI systems on cloud platforms that are observable, secure, and cost-aware — not fragile snowflake servers.
Summary
Cloud architecture, DevOps, containers, and security hardening for AI and product workloads.
Problem
What teams are stuck with
AI prototypes explode cloud bills; production has no autoscaling or budget alarms.
Security and secrets are improvised until an audit or incident.
Offerings
What we deliver
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01
Cloud architecture and migration
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02
DevOps and platform engineering
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03
Kubernetes and containers
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04
Security and compliance hardening
Approach
How we work it
Infrastructure as code, environments as cattle, observability as default.
Separate inference spend from product spend with clear budgets.
Least-privilege identity for humans, services, and agents.
Outcomes
What good looks like
- Predictable deploys and rollbacks
- Controlled AI and compute spend
- Audit-ready baselines
Sub-services
Inside Cloud Engineering
- Cloud Architecture & Migration Target architectures and migrations to AWS, GCP, or Azure for product and AI stacks.
- DevOps & Platform Engineering CI/CD, environments, and internal platforms so teams ship safely every day.
- Kubernetes & Containers Container platforms for scalable APIs, workers, and inference sidecars.
- Security & Compliance Hardening Identity, secrets, logging, and baseline controls for product and AI systems.
Related
Keep exploring
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