A note on evidence
We do not publish anonymized case studies with invented metrics. If you want references or a detailed walk-through of comparable work, ask during the first conversation and we will talk specifics with the people who did it.
Stabilizing a Kubernetes environment
What we usually see
- Pods restart for reasons nobody has traced
- Cluster upgrades are deferred because they feel risky
- Resource requests and limits were guessed once and never revisited
- Autoscaling behaves unpredictably under load
How we approach it
Establish what the cluster is actually doing, fix the failure modes that recur, get resource and scaling configuration onto a rational basis, and bring cluster and add-on upgrades into a repeatable process.
- EKS
- Kubernetes
- Helm
- Karpenter / autoscaling
- Prometheus
Modernizing Terraform and Infrastructure as Code
What we usually see
- Console changes have drifted from what the code describes
- One large state file makes every change feel dangerous
- Copy-pasted configuration across environments
- No one is confident running a plan against production
How we approach it
Reconcile drift, split state along sane boundaries, factor shared configuration into modules, and put changes behind a plan-and-review workflow so infrastructure changes stop being events.
- Terraform
- Terraform modules
- Remote state
- Policy and review gates
Improving CI/CD and delivery
What we usually see
- Deploys need a specific person or a manual runbook
- Pipelines are slow enough that engineers batch changes
- Rollback means redeploying an older branch and hoping
- Build configuration differs per service for no deliberate reason
How we approach it
Standardize how services build, test and deploy; make pipelines fast enough to run on every change; and make rollback an ordinary, rehearsed operation rather than an incident response.
- GitHub Actions
- GitLab CI
- Container builds
- Deployment automation
Migrating workloads to AWS
What we usually see
- Workloads sit on infrastructure that is expensive or unsupported
- A migration has been planned repeatedly but never owned
- Uncertainty about which AWS services fit the workload
- Cutover risk is unclear, so the decision keeps sliding
How we approach it
Assess the workload and its dependencies, choose target services deliberately, build the target environment in code, and run a rehearsed cutover with a defined rollback path.
- AWS
- VPC and networking
- RDS
- ECS / EKS
- Terraform
Building observability
What we usually see
- Incidents are discovered by customers first
- Dashboards exist but nobody trusts them
- Logs are collected but hard to search when it matters
- Alerts are noisy enough that people have stopped reading them
How we approach it
Instrument what actually indicates health, consolidate logs and metrics where engineers will look, and reduce alerting to signals a human should act on.
- CloudWatch
- Prometheus / Grafana
- OpenTelemetry
- Centralized logging
- Alert routing
Reducing operational toil
What we usually see
- Routine work is manual, repetitive and undocumented
- The same questions reach the same engineer every week
- Onboarding an environment takes days of tribal knowledge
- Application engineers spend significant time on infrastructure
How we approach it
Identify the recurring manual work, automate what should never be manual, document what must stay manual, and move routine requests onto self-service paths.
- Automation scripting
- Terraform
- Internal tooling
- Runbooks
Troubleshooting difficult production infrastructure issues
What we usually see
- Intermittent failures that nobody has been able to reproduce
- Latency or networking problems that cross service boundaries
- Symptoms that appear under load and vanish under inspection
- An issue that has been open long enough to become normal
How we approach it
Work the problem systematically — narrow it with evidence from logs, metrics and configuration rather than by guessing — then fix the cause and leave behind the instrumentation that would catch it next time.
- AWS networking
- Kubernetes internals
- Log and metric analysis
- Load testing
Improving security and reliability posture
What we usually see
- IAM permissions are broader than anyone intended
- Backups exist but restores have never been tested
- No agreed process for patching or vulnerability remediation
- Single points of failure that everyone knows about and nobody owns
How we approach it
Tighten access to what is actually needed, harden the infrastructure layer, verify that recovery works by testing it, and remove the failure points that would matter most.
- IAM
- AWS security services
- Backup and recovery
- Patch and remediation workflow