Cloud and DevOps that make deployments routine and costs visible.
We design cloud architecture, define infrastructure in code, automate build and release, and set up the monitoring and cost reporting a production system needs.
- Cloud architecture
- Infrastructure as code
- CI/CD pipelines
- Monitoring and incident response
- Security baseline
- Cost visibility
Where this helps
What we deliver
How it works
- 01
Assess
We review current infrastructure, deployment practice, incidents and spend, and agree the priorities.
- 02
Design
We define the target architecture, account structure and pipeline standards before changing anything.
- 03
Codify
Existing and new infrastructure is captured in code, starting with the environments that change most.
- 04
Automate delivery
Pipelines are introduced application by application, with the team deploying through them as soon as each is ready.
- 05
Operate and hand over
Monitoring, on-call practices and documentation are handed to your team, or we continue under a support agreement.
Design decisions we make with you
Cloud provider
We usually build on the provider you already use. Multi-cloud is recommended only where there is a concrete requirement, because it adds cost and complexity.
Containers, serverless or VMs
Managed and serverless services reduce operational work for many workloads. Containers and Kubernetes suit teams running many services with the skills to operate them.
Environment strategy
How many environments, how production data is handled outside production and who can deploy where.
Reliability targets
Service objectives are set from business impact, so effort on redundancy and recovery matches what downtime actually costs you.
Ownership
Infrastructure lives in your accounts and your repositories, with access for our team granted and revoked by you.
Applications
Related capabilities
- Backend & API DevelopmentServices, APIs and data models with sound authentication, integrations, testing and operational reliability.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
- Application ModernizationAssess legacy systems, then refactor, replatform or rebuild the parts that need it — migrating in stages to limit disruption.
- CybersecurityApplication security, testing and secure engineering for the software and AI systems you build and run.
Questions buyers ask
Not necessarily. Managed container services and serverless platforms cover many workloads with less operational overhead. Kubernetes is worth it when you run many services and have the team to operate it.
We make spend visible by application and environment, then identify unused resources, oversized instances and pricing options. How much can be saved depends on your current setup, and we report findings before making changes.
Yes — including model serving, GPU capacity and private deployments. See MLOps and LLMOps and enterprise private AI.
Through named, least-privilege roles that you grant and can revoke at any time, with actions logged. See trust and security.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.