Combine sound cloud architecture with AI-assisted incident investigation and capacity analysis. Build reliable foundations for applications, AI workloads and agents.
An example of what we can scope with your team. The tools and automation boundaries follow your workflow.
Correlate resource usage, workload behavior and cost signals.
Use AI-assisted analysis to identify changes worth testing.
Apply reviewed configuration and verify health and spend.
Cloud priorities, reviewed infrastructure changes and operating checks.
Depending on fit: Azure · AWS · infrastructure as code · observability
Choose a defined project or ongoing delivery. Engineering Capacity is one option when its scope fits. Scope and price are agreed first.
Organizations we’ve worked with
Infrastructure work for systems that need safer releases, predictable scale, and visible production behavior.
We design the environments, networks, storage, queues, identity, and runtime boundaries that keep automation, AI, and platform workflows stable.
We migrate workloads, isolate risk, validate data paths, and create rollback plans so cloud changes do not interrupt the operation.
We combine workload and cost signals with AI-assisted analysis to recommend capacity changes. Reviewed limits and budgets govern scaling.
We connect logs, metrics, traces, alerts, and ownership paths so production issues become visible before teams are forced to guess.
The cloud layer is designed around containment, visibility, and recovery, not just hosting.
Production, staging, secrets, access, and network boundaries are separated so changes do not leak across environments.
Deployments include rollback paths, data protection, and validation checks before critical workflow changes go live.
Queues, workers, rate limits, retries, and fallback paths keep dependency failure from spreading through the system.
Usage signals, resource budgets, and scaling rules make cloud spend and capacity pressure visible before they become business problems.
See infrastructure and reliability work in production systems.
50x
deployment frequency
Biomark
CI/CD, infrastructure, and monitoring were rebuilt to support safe releases across a multi-app healthcare platform.
KlaroReliability Engineering
50,000 users supported, 10,000+ sensors connected, and 99.2% uptime.
Echo Drive LogisticsReliability Engineering
90% automation coverage, 70% less manual testing, and 85% fewer production issues.
A clear starting point
Identify the immediate infrastructure risk and agree a project. Engineering Capacity is available for ongoing delivery; monitoring and on-call cover require their own agreement.
Reviewed architecture, prioritised changes and agreed health, cost and recovery checks.
Migration scope includes cutover ownership, data validation and recovery objectives. Provider costs remain separate.
Review my cloud setupSomething already broken? Explore System Rescue.
The result is infrastructure your team can trust when automation, AI, and platforms become business-critical.
Cloud environments support staged rollout, validation, and rollback so changes are less likely to disrupt live workflows.
Monitoring is connected to ownership and recovery actions, reducing time lost to unclear failures.
Systems handle demand changes through planned capacity rules instead of emergency infrastructure work.
Access, secrets, network boundaries, and dependency behavior are designed to contain failure and protect critical data paths.
By industry
Explore the systems and operating needs that shape delivery in your sector.
Explore all industriesClient feedback from delivery engagements — see all reviews
"We outsourced our ERP development to Octacer, and they delivered a perfect solution that integrated smoothly with our existing systems. Their team is excellent."
Imran Rashid
CEO, My Construction
Scope, delivery and what happens next. The details that help you decide.
Yes, subject to an access review. We agree least-privilege roles, change approvals and account ownership before making changes.
We assess the current stack, team skills, residency needs and operating cost. Migration is not automatically necessary; improving the existing setup may be the better scoped project.
Not always. An application calling a model API may need conventional application infrastructure. Hosted inference adds model sizing, GPU capacity and utilisation decisions, which we assess separately.
We plan the cutover, validation and fallback around your availability needs. Any maintenance window and data-sync limitations are agreed; zero downtime is not assumed.
We agree recovery objectives, backup ownership and restore checks. A configured backup alone is not evidence that recovery works.
Your provider usage and licences are separate from engineering fees unless stated otherwise. Budgets and alerts can be part of the agreed implementation.
No automatic coverage is implied. Monitoring, response hours, escalation and incident ownership must be defined in a separate or explicitly included support agreement.
We review architecture, scaling pressure, deployment safety, and production blind spots, then outline what needs to change first.
20–30 minutes · No preparation needed · Free
