IT operations optimisation for organisations that want to stop firefighting as a business model
We optimise your IT operations so that changes become predictable, incidents become rare, and your engineers can get back to building features instead of paging. SRE principles, observability that works, and automation with a demonstrable ROI, not frameworks for the sake of frameworks.
The symptoms we see again and again before we start
IT operations is rarely a single problem. It is usually a collection of small inefficiencies that reinforce one another. Below are the patterns that typically point to a slow or fragile operation.
Releases people dread
A deployment is an event. People stay late, there is a war room, and sometimes a rollback. Engineers postpone releases to Wednesday because 'you don't deploy on a Friday'.
Mean time to recovery measured in hours or days
When something breaks, it takes a long time to get working again. Not because of complexity alone, but because of limited observability and no runbooks.
Incidents that keep recurring
The same problem at the same hour on the same day. Post-mortems without action items, or action items that nobody follows up on.
On-call rotas that drive people away
Engineers partly leave organisations because the pager goes off too often. Recruiting replacements becomes difficult because this is widely known.
Cloud bills spiralling out of control
Nobody knows exactly why costs rise 8% month on month. Tagging is partial, there is no FinOps practice, and idle resources keep running.
Compliance as a fire-fighting exercise
An audit is coming, and only then are access reviews, logging and encryption rushed into place. Once it's over, attention drifts elsewhere.
Our approach: measure where it hurts, fix what matters
No generic "DevOps transformation". We start with the numbers — DORA metrics, MTTR, change failure rate, costs — and focus on the three to five interventions with the highest expected impact.
Operational assessment
3–5 weeks: we map your deployment pipeline, observability stack, on-call organisation, change management and incident history. The output is a report with a DORA baseline, the top pain points and a prioritised list based on effort and impact.
Quick wins first
4–8 weeks: low-effort, high-impact interventions — automated deployments for one component, trimming alerting down to actionable signals only, meaningful SLOs for one service, and runbooks for the top three incident types.
Observability that works
2–3 months: tracing, metrics, structured logging and SLO dashboards across the entire stack. Not as a showcase for management, but so that engineers know within 30 seconds what is going on during an incident.
Gradual automation
Throughout the engagement, manual operations are captured in code (Terraform, Ansible, GitOps), test coverage grows (unit, integration, smoke, end-to-end), and deployments move from manual-with-a-checklist to one-click or automated on green.
On-call culture and runbooks
Pager load is analysed, alerts that don't lead to action are switched off, and runbooks per service become reviewable artefacts in the repository. On-call rotations are shared fairly and pager fatigue receives structural attention.
FinOps and cost control
A tagging strategy, budget alerts per team, autoscaling where it makes sense, reserved instances and savings plans where justified, and a monthly FinOps review so that waste is spotted early.
What we optimise — and what we advise against
Not every "best practice" suits every organisation. We help you choose which operations investments deliver value and which you can comfortably skip.
What we typically tackle
What we advise against
The four pillars of healthy IT operations
The order in which you tackle them differs by organisation, but without these four pillars IT operations will keep fighting entropy.
1. Predictable deployments
Every change follows the same automated pipeline. No manual steps, no 'special' release procedures for production. Rollback is a button, not a phone call. Frequency goes up (daily or more), batch size comes down.
2. Observability as a first-class concern
Logging, metrics and tracing are standard — not an afterthought. Engineers can dig deeper on their own than the dashboards show. SLOs are defined from the user's perspective, not from technical metrics such as CPU%.
3. Incident management that drives learning
Postmortems are blameless and produce concrete action items. Action items have owners and deadlines. Patterns across incidents feed back into architecture decisions and SLO adjustments.
4. Capacity and cost discipline
Tagging is complete. Every team knows what their infrastructure costs. FinOps is a shared responsibility, not a department playing catch-up with the facts. Where it makes sense: reserved capacity, autoscaling, right-sizing.
Tech stack we often implement
Pragmatic choices with strong communities and long support roadmaps. We don't want tools that are dead two years from now.
CI/CD and GitOps
GitHub Actions, GitLab CI, Azure DevOps for pipelines. Argo CD or Flux for Kubernetes deployments. Renovate for dependency updates.
Infrastructure as code
Terraform (including OpenTofu) for multi-cloud, Bicep for Azure-only, Pulumi where TypeScript-based IaC suits the team. Ansible for configuration management.
Observability
Datadog (managed), Grafana Cloud, or self-hosted Grafana, Prometheus, Loki and Tempo. OpenTelemetry as the standard for instrumentation so you can switch without rewriting code.
Incident management
PagerDuty or Opsgenie, statuspage.io or incident.io. Slack integrations for war rooms. Runbooks in the repository, not in a wiki nobody maintains.
Kubernetes
EKS, AKS or GKE with a cluster baseline (network policies, pod security, OPA/Kyverno, cert-manager, ingress-nginx or Traefik). Karpenter or cluster-autoscaler for scale.
FinOps
CloudHealth, Vantage or native cost explorers. Tagging policies via OPA, budget alerts via Slack, monthly review meetings with team owners.
What you typically see after 6 to 9 months of operations optimisation
Not always, and not for everyone, but these are patterns we see again and again when the right interventions are made and management stays committed.
Deployment frequency up, batch size down
From weekly releases to daily or more often. Fewer changes per release means fewer chances of regressions. Engineers dare to ship smaller pieces because the pipeline allows it without penalty.
Mean time to recovery in minutes
What used to take hours is often resolved in 15 to 30 minutes. Not through miracles, but through better observability, clear runbooks and automatic rollbacks where possible.
Pager load halved or more
By rebuilding alerting from an SLO perspective, most flapping alerts disappear. On-call engineers sleep through the night, except for real problems, and then they have enough context to act quickly.
Cloud costs under control
No miracles ("we save 70%"), but a realistic 15 to 30% reduction through right-sizing, idle cleanup, the right reservations and better autoscaling. More importantly, costs become predictable and attributable.
Engineering satisfaction rises
Engineers who spend more time on features than on incidents are measurably happier. Turnover falls, recruitment becomes easier and knowledge stays in-house longer.
Frequently asked questions about IT operations optimisation
What is the difference between DevOps, SRE and platform engineering?
DevOps is a culture and set of practices (collaboration between development and operations, automation, fast feedback). SRE is a specific implementation with a strong emphasis on SLOs, error budgets and engineering discipline. Platform engineering focuses on building internal developer platforms so that application teams can work independently. In practice they overlap considerably, and we help clients choose what suits their level of maturity.
How long does a project like this take?
An operations assessment plus initial quick wins usually takes 2 to 3 months. A full optimisation (observability, incident management, automation, FinOps) runs 6 to 12 months, depending on scope and organisational readiness. It is a journey with continuously measurable progress, not a big bang.
Do we need to move to the cloud before we start on this?
Not necessarily. SRE principles and observability are just as relevant on-premises. Managed cloud services do reduce operational burden, though, so for some optimisations a partial cloud migration is the sensible order. We'll be honest about what pays off first.
What do you do about cloud costs?
We introduce FinOps discipline: a tagging strategy, cost attribution per team, budget alerts, regular right-sizing reviews, and well-founded decisions on reserved instances and savings plans. Realistically, we expect a 15-30% reduction without any impact on service, sometimes more. Promises of 70% savings are usually sales talk.
Do you work alongside our internal ops people?
As standard. Optimisation your team can't maintain isn't optimisation. We work in mixed teams, document architectural decisions in ADRs and make knowledge transfer explicit. The end goal is for your team to keep things running without us.
What if we have Kubernetes that is hard to manage?
That is often a symptom of too much custom infrastructure. We carry out a Kubernetes audit (cluster design, networking, security, ingress configuration, autoscaling), identify what is unnecessarily complex and replace or simplify it. Sometimes the best action is to move away from self-managed Kubernetes towards a managed tier (Cloud Run, App Service, ECS Fargate) for parts of the workload.
What does operations optimisation cost?
An assessment typically falls between €15,000 and €35,000. A full programme (depending on scope, scale and which pillars take priority) runs from €80,000 to several hundred thousand euros. You'll receive a concrete estimate after the assessment. We don't take on programmes without concrete, measurable goals.
Can you help with SOC 2, ISO 27001 or NIS2 readiness?
Yes, operational practices bear directly on compliance (logging, change management, access reviews, incident response). We know the requirements and can set up your operations so that audits pass without panic. We are not an audit firm; we deliver the operational substance an auditor can test against.
Two components almost always belong here: observability and monitoring, so that recovery time isn't dictated by searching for the problem, and vulnerability management with agreed remediation deadlines.
Ready to move your IT operations from reactive to predictable?
We start with an assessment conversation. You'll get an honest picture of your DORA baseline, the pain points with the greatest impact and a concrete plan for the first three to six months. Practical, not sales talk.