Complexity
starts where
vision stops.

We design and build Red Hat OpenShift across bare-metal, virtualised and managed deployments, matched to the workload and the constraints around it rather than a single reference architecture repeated everywhere. That includes cluster topology, node lifecycle, storage and networking decisions, and the upgrade path that keeps a platform current without becoming an event. We size for the failure modes that matter to the organisation running it, not the ones that are easiest to demonstrate, and we leave behind a platform its own team can operate, extend and upgrade with confidence.

We design and implement Ansible Automation Platform as the operational backbone of an environment — provisioning, configuration, patching and remediation expressed as version-controlled, peer-reviewed automation rather than a growing pile of one-off scripts. That means role and collection structure that scales past the first few playbooks, execution environments that behave the same in test as in production, and access controls that match who is actually accountable for what. The result is automation an operations team can read, trust and extend once we are gone.

Resilience work only counts once it has been rehearsed. We design backup, replication and failover architecture around a recovery objective the business has actually agreed to, then prove it with recovery exercises rather than a document nobody has opened under pressure. That includes disaster recovery runbooks written for the person on call, recovery time and recovery point targets grounded in real constraints, and the periodic testing that keeps a recovery plan honest as the environment beneath it keeps changing.

We design network and platform segmentation for organisations that must be able to explain their security posture to an auditor, a regulator or their own board — zone architecture, east-west traffic control and identity boundaries built around what a threat model actually requires, rather than a checklist. Every design decision is documented with its rationale, so the segmentation holds up under review months or years later, and the team inheriting it understands the reasoning, not only the configuration.

We build GitOps delivery pipelines and the observability stack around them so that what is running matches what is declared, and drift is caught before it becomes an incident. That covers pipeline design across build, test and progressive delivery, GitOps controllers configured for genuine rollback behaviour, and metrics, logging and tracing set up to answer the questions an operations team will actually ask under pressure, not only the ones a dashboard looks good answering.

Our AI-operations practice grew out of tooling we built to run Techsage’s own delivery work — automation that drafts, reviews and monitors under the same documentation and review standard we hold for a client platform. Where it earns its place in an engagement, we bring the same approach to an organisation’s operational workflows: automating what is well understood, recording what an automation decided and why, and keeping a senior practitioner accountable for the outcome rather than the model.