Discovery, deployment and run for the platforms your estate depends on — Elastic, Red Hat and OpenShift, SUSE, Tricentis, UiPath — plus the infrastructure, observability and SecOps work that keeps them honest at three in the morning.
Most firms in our band sell the build and hand you the keys. This practice exists because the harder half of the value — and all of the risk — sits after go-live.
We take a platform someone else wrote, fit it to your estate, tune it against your actual traffic, and then stay accountable for it: upgrades, capacity, cost, incidents, the lot. That means we have to understand the platform as engineers rather than as a licence line, which is why we are selective about which ones we carry.
It also means we will tell you when the answer is fewer tools rather than more. Observability estates in particular have a way of accumulating three products that each see two-thirds of the picture.
One telemetry pipeline serving both the operations team and the security team, rather than two that disagree. Elastic-centred design, ingestion and retention economics, detection rules, dashboards people actually open, and the on-call practices around them. Delivered for capital markets, manufacturing and government estates.
Moving workloads off what they are on today without pretending the migration is the goal. Red Hat and OpenShift, SUSE, Kubernetes, hybrid and private cloud. Actionable roadmaps for legacy-to-cloud migration, sequenced so each step is independently valuable.
Monitoring, event correlation, runbooks and self-healing automation, so that the routine incidents stop reaching people. Where the process is stable and high-volume, we automate it with TurfAI and UiPath rather than staffing it.
The TurfAI practiceCost is an architecture problem before it is a procurement problem. Workload right-sizing, data-layer redesign, storage tiering and commitment strategy across AWS, Azure and private cloud — measured against your pre-engagement monthly bill, not against a vendor calculator.
Batch and streaming pipelines, lake and warehouse design, schema management and lineage, and the quality monitoring that says when a source has quietly changed shape. Built to be observable, because a pipeline nobody watches is a pipeline that has already broken. Where an AI system sits downstream, this is the layer that decides whether it is right or confidently wrong. We would rather build it first than retrofit it around a model that is already in production.
Discovery, licensing guidance, deployment, enablement and managed run for the platforms in our partner practice. Where a platform is the right answer we will say so; where your own build would be cheaper over three years, we will say that instead.
The partner practiceAn agreed service level, a named team, published response times, and a monthly review that covers cost and drift as well as uptime. The point of this practice is that someone answers the phone.
Each of these represents a team that has deployed it, broken it in a lab, and run it in production for someone else.
Elastic · Red Hat & OpenShift · SUSE · Tricentis Tosca · UiPath · Kubernetes · AWS · Microsoft Azure · MongoDB · Grafana
Tell us what it is, what it costs you a month, and what happens when it breaks. We will come back with what it would take to run it properly.
